Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

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Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

♻️ Share this with your network
🔔 Follow me (Aishwarya Srinivasan) for no BS data and AI updates and insights

This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Thanksgiving week, yet a lot happening in this week in AI!
Catch everything you need to know here 👇

Vision-Language Models connect what AI sees with what it reads and reasons.

They’re the foundation of AI systems that can interpret charts, medical images, retail shelves, or product catalogs.

But a generic VLM doesn’t understand your domain’s visual language.
That’s where fine-tuning becomes essential.

𝐖𝐡𝐲 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐚 𝐕𝐋𝐌 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

A pretrained VLM already knows the basics of visual-text reasoning.
Fine-tuning helps it specialize for your domain.

→ In healthcare, it learns to detect anomalies in MRIs and X-rays.
→ In retail, it interprets shelf images and product layouts.
→ In enterprise, it extracts structured data from invoices and reports.

You’re not rebuilding intelligence, you’re refining perception to fit your use case.

𝐇𝐨𝐰 𝐋𝐨𝐑𝐀 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭

Full model fine-tuning is expensive and compute-heavy.
Low-Rank Adaptation (LoRA) keeps the base model frozen and trains only small adapter layers.

That means:
→ Faster training cycles
→ Smaller memory footprint
→ Lower compute costs
→ Domain adapters that are easy to swap in and out

You can maintain one base model and multiple lightweight adapters for each use case such as invoices, medical forms, or retail analytics.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐚 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥

A VLM has three main components:
→ 𝐕𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐜𝐨𝐝𝐞𝐫 converts pixels into visual tokens.
→ 𝐅𝐮𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 combines visual and text context for reasoning.
→ 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐃𝐞𝐜𝐨𝐝𝐞𝐫 generates captions, summaries, or structured responses.

Each layer introduces potential failure modes like poor resolution, misaligned regions, or verbose hallucinations. Fine-tuning improves alignment and reliability across these components.

𝐓𝐡𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞

→ 𝐃𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: Collect diverse, high-quality, clearly labeled visuals.
→ 𝐓𝐚𝐬𝐤 𝐝𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 : Choose the right setup: captioning, VQA, extraction, or localization.
→ 𝐋𝐨𝐑𝐀 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Train adapters for each domain efficiently.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Use both quantitative metrics and human review for grounding and accuracy.

Always evaluate across different slices such as document type, lighting, and template to surface hidden biases.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Each domain adapter should have its own dataset lineage, version, and evaluation score.
Reliability requires attention to fairness, privacy, consistency, and uncertainty handling.
Fine-tuning doesn’t just improve accuracy, it strengthens governance and ethical alignment.

LoRA fine-tuning makes VLMs faster to adapt, cheaper to deploy, and more aligned with your real-world data. 

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Career Coach for Senior Professionals | Job Search Strategist | Resume & LinkedIn Expert | 20+ Yrs in Talent Acquisition | 1000+ Careers Transformed

You’re not behind. You’re just not positioned the way the leadership market expects — yet.

I’m Meenu, a Career Transformation Coach and Job Search Strategist helping senior professionals (35–60 age group, Director to CXO) break through stagnation, reframe their value, and land the roles they’ve already outgrown on paper.

I bring 25+ years in global talent acquisition across Accenture, Hewitt, and CIBC — interviewing, hiring, and evaluating thousands of leaders across business, tech, operations, HR, finance, and transformation.
Today, I use that inside knowledge to help senior professionals navigate:

• Leadership transitions
• Career pivots across industries
• Returning after a career pause
• Ageism and stalled visibility
• Plateaued growth or unclear direction

My 5-Step Career Accelerator is built on how hiring actually works. It helps you:
✓ Get absolute clarity on your next move
✓ Rebuild your Resume & LinkedIn for senior-level visibility
✓ Strengthen your narrative for high-stakes interviews
✓ Create a leadership presence that attracts the right rooms
✓ Activate a strategic, modern job search (no mass applying, no guessing)

Results:
• 1000+ careers transformed globally
• 70% of clients land interviews in 4–6 weeks
• 2–3× LinkedIn visibility in 45 days
• Successful transitions into Director, VP, and CXO roles across tech, consulting, banking, finance, operations, and nonprofit sectors

If you’re a mid-to-senior professional ready for your next leadership chapter — with clarity, confidence, and a compelling story — I’d love to help.

👉 Book a discovery call: calendly.com/meenu-careercoach/discovery-call
👉 Or DM me directly.

#CareerCoach, #JobSearchStrategy, #ExecutiveBranding, #ResumeTips, #LinkedInOptimization

Meenu Chadha

I don’t talk about this often, but after 20 years in talent — and seven career transitions of my own — here’s the piece I wish someone had told me much earlier:

Most of us don’t struggle because we lack experience.

We struggle because we outgrow our story long before we realize it.

I’ve sat in rooms with VPs who quietly admitted they didn’t feel as confident as their title suggested.

I’ve coached brilliant leaders who whispered, “I don’t even recognize the version of me on my resume anymore.”

And I’ve lived that gap myself — the one between who you were, who you’ve become, and who you’re scared to say you want to be next.

It took me a long time to see it:
The market doesn’t reward the years I stayed.

It rewards the evolution I survived.
It rewards the reinventions I didn’t plan for.
The problems I neutralized when everything felt unstable.
The value I created on days I felt like an imposter.
The change I carried even when no one saw the cost.

But I used to hide all of that.

Most senior professionals do.

We shrink the messy parts.
We over-index on the “safe” parts.

We cling to the older story because it feels less risky than telling the truth.

Yet every turning point in my own career — and every breakthrough I’ve witnessed in others — started with one uncomfortable realization:

Your story isn’t finished.
 
But the one you’ve been telling is no longer you.

If you’re feeling that quiet wobble, that tension, that sense of being “bigger on the inside than what your profile shows”… you’re not alone. I’ve lived it. My clients live it. Nearly every leader I’ve ever coached has lived it.

If you’re ready to tell the version of your story that feels honest, evolved, and aligned with the leader you’ve actually become, DM “STORY.”

Career Transformation Coach for Senior Professionals | Job Search Strategist | 25+ Yrs in Global Talent Acquisition (Accenture • CIBC • Hewitt) | Executive Resume & LinkedIn Expert

Two executives with identical backgrounds.

Same industry. Same seniority. Same market timing.

Executive A: 347 applications over 14 months.

Executive B: 73 applications over 6 months.

Guess who landed the $280K role?

The difference wasn't persistence.

They both had that in spades.

The difference was strategy.

Executive A was spinning wheels:

↳ Mass applying to job boards
↳ Same resume for every role  
↳ No follow-up system
↳ Networking without purpose

Executive B was strategically persistent:

↳ Targeted 15 companies maximum
↳ Customized positioning for each
↳ Systematic stakeholder mapping
↳ Strategic relationship building

Here's what most executives miss:

Working harder isn't the answer.

Working smarter is.

You can send 1,000 applications and hear nothing.

Or send 50 strategic ones and land multiple offers.

After 300+ transitions, I've watched this exact pattern repeat.

The executives who land fastest?

They don't outwork the competition.

They outthink it.

Strategic persistence beats blind hustle.

Every time.

If you're exhausted from spinning your wheels, DM "REFRAME" and I'll share the targeting framework that turns effort into offers.

Attach to a company and a layoff can break you.
Attach to a mission and a layoff redirects you.

Power comes from purpose. That image says it all.

What she was doing (and why it stalled):
25–30 “Easy Apply” clicks/day. Zero warm paths.
4-page, task-heavy résumé sent everywhere.
Headline: “Senior Ops Professional | Open to Work.”
Considering a two-level step down just to get back in.

The market is tough. I see it daily—strong leaders second-guessing themselves and settling for less.

Here’s the shift we made—purpose over panic (REFRAME™):
Operating Thesis (1 page): 3 problems she owns + proof + 90-day ROI.
Executive Time System: decision rights, cadence, dashboards, stop-doing list.
Business-case résumé: metric → lever → outcome (not a biography).
Targeted motion: 30 companies • 3 warm paths each • 15 quality touches/week.

What purpose unlocked:
 → VP, Strategy & Operations
 → Executive staff seat, owns the operating cadence
 → +80% total compensation + relocation support

The pattern?
 Job-attached = fragile.
 Mission-attached = antifragile.

See the difference?
If you’re searching right now, remember:
You lost a job, not your calling.
You’re between roles, not without purpose.
You’re building a career, not begging for work.

Companies don’t hire desperation. They hire conviction.
Show them your mission, not your situation.

If you’re a senior leader still spraying and praying, message REFRAME.
 I help leaders land fulfilling $200K–$500K roles through strategy—not chaos.

Job Search Coach |Career Coach for Senior Professionals |Resume & LinkedIn Expert | 20+ Yrs in Talent Acquisition | 1000+ Careers Transformed

Job searching tests something no interview ever will: your ability to stay grounded when everything feels uncertain.

The rejections.

The waiting.

The silence.

What we don't talk about enough?

The emotional toll of it all.

But here's the reminder I carry with me—and one I share with every client:

Your career is a part of your life. It's not the whole of it.

This picture?

A quiet pause outside a local café between client calls.

No rush. No hustle. Just presence.

Because staying grounded is a strategy too.

✔ Maintain your routines

✔ Nurture your energy

✔ Don't put your joy on hold while you search

The most compelling candidates aren't the most desperate.

They're the most balanced.

Your next employer isn't looking for someone who needs them. They're looking for someone who chooses them.
#CareerCoach #WorkLifeBalance #LeadershipTransitions #CareerGrowth

“Can you find me a job?”

“I’m looking for roles — can you help?”

These don’t get responses.

Not because you lack capability — but because that’s not how recruiting works.

Here’s the truth most people never get told:
Recruiters don’t work for candidates.
They work for employers.

Their responsibility is to fill specific open roles inside an organization — not to run your job search, explore the market on your behalf, or “find something” that fits.

So when your outreach is vague, generic, or places the burden on them, you’re unintentionally asking them to do a job they were never hired to do.

If you want to get noticed, shift from “Can you help me?” to “Here’s how I align with the roles you hire for.”

And this is where most messages fall apart.

The screenshot you see is polite — but it’s directionless.
A recruiter simply can’t act on it.

The outreach that actually gets traction includes:
• The specific roles you’re targeting
 • Your industry or domain focus
 • Your location or preferred geography
 • 2–3 compelling strengths that match their hiring scope
 • A clean resume or strong LinkedIn link

Recruiters respond to relevance, not requests.
Precision, not hope.
Value, not volume.

Master this shift, and your outreach stops feeling like noise — and starts opening doors.

“I know I’m capable of more — but I’m not sure what the next step should be.”

Over the last year, I’ve had hundreds of conversations with senior professionals who felt the same quiet tension!

That sentence has become the starting point for some of the most powerful transformations I’ve witnessed.

So I decided to create a place to share those insights more openly.

Introducing Connect the Dots — my new newsletter for senior professionals navigating pivots, clarity gaps, and leadership transitions.

If you want deeper conversations on identity, positioning, and strategy — this space is for you.

 Subscribe on LinkedIn https://lnkd.in/gwqKbe6c

Meenu Chadha Career Transformation Coach for Senior Professionals | Job Search Strategist | 25+ Yrs in Global Talent Acquisition (Accenture • CIBC • H

Open article: 🟦 When Your Capability Outgrows Your Title by Meenu Chadha • 3 min read

🟦 When Your Capability Outgrows Your Title

“I know my LinkedIn is outdated… I just don’t know where to start.”
“I’m embarrassed to put myself out there.”
“I’ll update it when I feel more confident.”

I can’t tell you how many VPs, Directors, and seasoned leaders whisper the same thing to me !

The biggest career sabotage I see in senior professionals has nothing to do with skills, strategy, or the market.

It’s the quiet fear of being seen.

And that’s exactly where the derailment begins.

Because here’s the part no one talks about:
Most incomplete profiles aren’t a time issue.
They’re a confidence issue.

We tell ourselves we’re “busy,”
but underneath it all…
we’re afraid our story won’t measure up.

Meanwhile 90%+ of recruiters are searching on LinkedIn —
scrolling, scanning, deciding in seconds.

If your profile is half-written, half-visible, half-you?
You’re not just hard to find.
You’re hiding in plain sight.

Start small.
Five minutes today.
Five minutes tomorrow.
Update one section. Then another.

You don’t need a perfect profile —
just the courage to stop disappearing from your own career.

Because incomplete profiles don’t get calls.
But complete ones?
They start conversations you’re more than ready for.

If you want my 5-Minute LinkedIn Visibility Checklist, DM Checklist.
#career #linkedIn #careerchange

"I just need one more certification..."

"Let me take another course first..."

"My resume isn't perfect yet..."

Quick reality check:
 ✖ Perfect candidates don’t exist
 ✖ Perfect timing doesn’t exist
 ✖ Perfect readiness doesn’t exist

What actually works?
✅ Action beats preparation
 ✅ Experience beats credentials
 ✅ Growth mindset beats perfection

While you’re waiting to be “ready”…

Someone else is already in the room — learning, applying, growing.

Stop waiting to be perfect.

Start being purposeful.

 📩 Or DM "OPPORTUNITY" to explore what roles might align with your next move

#CareerGrowth #Leadership #careertransition

They didn’t reject you.
They just weren’t ready for your value.

“I don’t like to talk about myself.”

I hear this often from senior leaders.

But here’s the truth:
At your level, competence is assumed.
What sets you apart is how you position your impact.

Quick reality check:
→ A $200M turnaround means nothing if you can’t articulate the “how” and “why”
→ Leading 5,000 people won’t stand out unless you connect it to measurable outcomes
→ Strategy without storytelling gets filtered out

Because after 20+ years in talent, I’ve seen the pattern:
Executives who stay silent about their results get overlooked.
Executives who frame their results as business impact get opportunities.

The reframe?
You’re not bragging when you share achievements.
You’re demonstrating the transformation you can deliver next.

At the executive level, the market doesn’t reward modesty.
It rewards narratives that show strategic impact.

👉 Senior leaders: What’s one achievement you’ve minimized that deserves to shape your next chapter?

Two Identical resumes.
One landed $200K.
One got rejected.

Quick reality check:
It wasn’t skills. It wasn’t pedigree. It was storytelling.

Two managers. Same company. Same experience. Same skills.
 → First candidate: stuck at $105K
 → Second candidate: secured $200K

The difference?
 A 10-minute story reframe.
 (The same framework 3 of my clients use to land offers.)

The 3 Career Reframes™
1. The Impact Reframe
 ❌ Before: “8 years in SaaS product management”
 ✅ After: “Scaled product revenue from $2M → $8M by cutting churn 40%”

2. The Problem-Solver Reframe
 ❌ Before: “Seeking senior growth role”
 ✅ After: “I help B2B startups cut CAC 40% in 60 days”

3. The Leadership Reframe
 ❌ Before: “Leading cross-functional teams”
 ✅ After: “Built remote teams that shipped 5 features used by 87% of Fortune 500 clients”

Here’s the truth after 20 years in talent:
 → Average skills + average story = average salary
 → Average skills + powerful story = premium salary

Your 10-Minute Action Plan
Pick your biggest win
Delete the fluff
Add numbers

Test your new pitch tomorrow

Because your story is already worth $200K.
You just need to tell it right.
Drop REFRAME below and I’ll send you the worksheet.

#CareerStrategy  #ExecutiveCoaching #JobSearch

You send 50 connection requests.

47 get ignored.

The 3 responses? Polite declines.

I watch this destroy executives every day.

Here's what I learned after 400+ executive hires at Accenture and now coaching 300+ transitions:

Job searching is harder than doing any job.

In your role, you have control:
↳ Clear KPIs to hit
↳ Teams that respond
↳ Budgets that deliver results

In job search, you have none of that.

You craft the perfect message.
Target the exact right person.
Send it at optimal times.

Radio silence.

Not because your message was bad.
Not because you're not qualified.

Because that VP has 200 unread LinkedIn messages.
That hiring manager is in back-to-back meetings.
That recruiter is juggling 15 searches.

Your perfectly crafted outreach?
It's competing with chaos.

Here's the shift that changes everything:

Stop measuring success by responses.
Start measuring success by consistency.

From the hiring side, I can tell you:
The executives who get noticed aren't the ones who send one perfect message.

They're the ones who show up consistently.
Who build presence over time.
Who understand this is a volume and timing game.

Your job isn't to get every response.
Your job is to stay in the game until the right timing aligns.

The executive who gets the role?
Often sent 200+ messages to get 10 meaningful conversations.

That's not failure.
That's how this works.

DM "LINKEDIN TEMPLATES" and I'll share the messaging framework that turns silence into strategic advantage.

“I need results NOW. 
Networking takes too long.”

That’s what a VP told me after 6 months of relentless applications—
 256 sent.
 2 replies.
 0 interviews.

Sound familiar?

When the pressure builds, it’s tempting to double down on job boards. 
But here’s the reality I see every week:
Your next opportunity isn’t sitting in a portal.
It’s sitting in someone’s inbox—waiting for a nudge.

Take a look at this:
Client A
256 applications
2 generic responses
0 interviews

Client B
 4 coffee chats
 3 referrals
 1 offer

Same market. Different approach.

If you’re still relying solely on applications, try this instead:
✔ Message one former colleague
 ✔ Engage with one relevant post
 ✔ Share one thing you're learning

Want the exact message that got my client in the door—plus templates you can personalize today?

DM me “Networking Templates” and I’ll send them over.

#LeadershipTransitions #JobSearchStrategy #NetworkingThatWork
#CareerGrowth #CareerCoach

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The Battle

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