Aishwarya Srinivasan

Fireworks AI

→ Scaling the AI Developer Community building AI production workloads on Fireworks AI
→ Leading startup + VC partnerships

Head of AI Developer Relations- Growth

Hustle Fund

Angel Investor (Angel Squad)

Lumen Orbit (YC S24)

Investor

Decompute

Og.ai

Advisory Board Member

Tile (YC W24)

Angel Investor

DynamoFL

topmate.io

Advisor

Illuminate AI

First of its kind volunteer mentorship platform for folks in Artificial Intelligence!
Illuminate - AI is a platform built to connect people from the Artificial Intelligence Community.We believe knowledge grows by sharing. We are driven to connect mentors, who would like to give to the community, to mentees, who are seeking guidance and motivation. The mentorship program is not limited to a senior individual mentoring a novice but is it also open to peer-mentorship. 
There is a lot to learn!
A platform is also for sharing thoughts and ideas in the space of Artificial Intelligence and its applications in real-world use-cases. 
Playlists available are Weekend Musings, AI Guild, What's Tuning and Cracking Data Science Interviews.

Founder | Mentor

Microsoft for Startups

Senior AI Advisor

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 inteeview 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.

Just delivered my 100th session talking about AI, and I couldn’t have picked a better place for it than The Next Web Conference (TNW) in Amsterdam 🇳🇱

Had to shuffle some things to make it happen, but giving my milestone talk in one of the coolest cities in the world? 100% worth it.

My keynote focused on:
Beyond the Model: Building Scalable Agentic Systems with Open Source AI

Here’s what I covered:

– Most teams are still focused on improving models
– But the real challenge is building systems that can actually do things reliably
– Open source gives us the tools, but it also requires a systems-first mindset

We also got into:

→ Moving from models to multi-agent systems 
→ Architectures that scale with real-world complexity
→ Why open source is an advantage, not a compromise
→ The latest announcements from Fireworks AI, including experimentation platform/ Build SDK, supervised fine-tuning v2, Reinforcement fine-tuning, and voice agents

Amsterdam was 🔥 and it was exciting to see so many people thinking beyond models, and into system-level innovation.

If you’re curious about what we’re building at Fireworks AI, or want to jam on agentic workflows, DM me!

Huge thanks to TNW for having me, and to everyone who came up to chat after. I also hosted a meetup post-talk and had some incredible conversations I’ll share in a follow-up post :)

AI@Fireworks AI | Global Speaker(100 sessions) | EB1-A Recipient | 550k+ Followers| LinkedIn Top Voice | Author- What's Your Worth? | Startup AI Advisor | Angel Investor | Ex-Google, Microsoft, IBM

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master.

It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs.

Let’s break down how RAG works, step by step, from an engineering lens, not a hype one:

🧠 How RAG Works (Under the Hood)

1. Embed your knowledge base

→ Start with unstructured sources - docs, PDFs, internal wikis, etc.
→ Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models)
→ Output: N-dimensional vectors that preserve meaning across contexts

2. Store in a vector database

→ Use a vector store like Pinecone, Weaviate, or FAISS
→ Index embeddings to enable fast similarity search (cosine, dot-product, etc.)

3. Query comes in - embed that too

→ The user prompt is embedded using the same embedding model
→ Perform a top-k nearest neighbor search to fetch the most relevant document chunks

4. Context injection

→ Combine retrieved chunks with the user query
→ Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama)

5. Generate the final output

→ LLM uses both the query and retrieved context to generate a grounded, context-rich response
→ Minimizes hallucinations and improves factuality at inference time

📚 What changes with RAG?

Without RAG:
🧠 “I don’t have data on that.”

With RAG:
🤖 “Based on [retrieved source], here’s what’s currently known…”

Same model, drastically improved quality.

🔍 Why this matters

You need RAG when:
→ Your data changes daily (support tickets, news, policies)
→ You can’t afford hallucinations (legal, finance, compliance)
→ You want your LLMs to access your private knowledge base without retraining

It’s the most flexible, production-grade approach to bridge static models with dynamic information.

🛠️ Arvind and I are kicking off a hands-on workshop on RAG
This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build.

Here’s what you’ll learn:
 → How RAG enhances LLMs with real-time, contextual data
 → Core concepts: vector DBs, indexing, reranking, fusion
 → Build a working RAG pipeline using LangChain + Pinecone
 → Explore no-code/low-code setups and real-world use cases

If you're serious about building with LLMs, this is where you start.
📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

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The freedom to dream, the freedom to build, the freedom to belong.

That’s what this country has given me. And today, I just want to say thank you.

When I came to the U.S. at 22, I was driven by nothing but ambition, curiosity, and hope. I had dreams, not just for myself, but for the life I wanted to give my family. 

I didn’t come from generational wealth. I came with a belief that if there’s a place where hard work meets opportunity, this was it.

Over the years, I’ve been able to:
→ Work alongside some of the brightest minds in the world
→ Build a life and career that once felt too far-fetched to say out loud
→ Dream bigger than I thought possible
→ Build financial freedom for myself and the people I love
→ And do all of this without being judged for where I came from or how I looked

In a world that often boxes you in, by your gender, your accent, your passport, this country allowed me to just be me. A woman, a builder, a creator, a hustler, an immigrant with big dreams and no safety net.

And that, to me, is what July 4th is about.
It’s not just about independence,
It’s about possibility.

The possibility to redefine your future.
The possibility to contribute, to collaborate, to create.
The possibility to belong, no matter where your story started.

I’m endlessly grateful for the freedom this country has offered me, the freedom to think, to speak, to build, and to dream without limit. 🇺🇸

Happy 4th of July to all ❤️

If you want to get started in learning about LLMs, here is a easy-to-follow roadmap, I would recommend 👇 

📍 Step 1: Fundamentals of Machine Learning & Deep Learning
↳ Machine Learning Course: https://lnkd.in/d_2iakPf
↳ Intro to Deep Learning: https://lnkd.in/dNE69D5j

📍 Step 2: Understanding Transformers & Attention Mechanism
↳ Transformers from Scratch: https://lnkd.in/dnu9WGqC
↳ HuggingFace NLP Course: https://lnkd.in/dMPWewTd

📍Step 3: LLM Pre-training, Fine-tuning & RAG
↳ LLM University: https://cohere.com/llmu
↳ Pre-training LLM Course: https://lnkd.in/dC9CkCSA
↳ Fine-tuning: https://lnkd.in/d2CnYHEJ

📍 Step 4: Applications of LLMs & AI Agents
↳ AI Agents Course: https://lnkd.in/d4K4CUbS
↳ Advanced Large Language Model Agents Course: https://lnkd.in/diBAcg_s 
↳ AI Agents Mastery: https://lnkd.in/daTH2_3u
↳ Multi AI Agent Systems with crewAI: https://lnkd.in/d8iu6pap

📕 Top Book Recommendations:
↳ Natural Language Processing with Transformers: https://amzn.to/4ibJAjP
↳ Deep Learning book: https://lnkd.in/dq9pXXtq
↳ AI Engineering by Chip Huyen: https://amzn.to/3FoO9ZA
↳ Building LLMs for Production by Louis-François Bouchard https://amzn.to/3Fan1Ob
↳ LLM Engineer's Handbook by Maxime Labonne: https://amzn.to/3Ffz6By

📚 Key Research Papers:
 📄 Attention Is All You Need: https://lnkd.in/dzGaatsJ
 📄 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://lnkd.in/da9CReM3
 📄 GPT-3: Language Models are Few-Shot Learners: https://lnkd.in/dHVGucdG
 📄 Retrieval-Augmented Generation for Knowledge-Intensive NLP: https://lnkd.in/d-Ajg2_w
📄 Scaling Transformers to 1 Trillion Parameters: https://lnkd.in/d2-JZ-js
📄 DeepSeek R1 paper: https://lnkd.in/dEcicM2Z

I constantly get recruiter reachouts from big tech companies and top AI startups- even when I’m not actively job hunting or listed as “Open to Work.”

That’s because over the years, I’ve consciously put in the effort to build a clear and consistent presence on LinkedIn- one that reflects what I do, what I care about, and the kind of work I want to be known for.

And the best part? It’s something anyone can do- with the right strategy and a bit of consistency.

If you’re tired of applying to dozens of jobs with no reply, here are 5 powerful LinkedIn upgrades that will make recruiters come to you:

1. Quietly activate “Open to Work”

Even if you’re not searching, turning this on boosts your visibility in recruiter filters.

→ Turn it on under your profile → “Open to” → “Finding a new job”

→ Choose “Recruiters only” visibility
→ Specify target titles and locations clearly (e.g., “Machine Learning Engineer – Computer Vision, Remote”)

Why it works: Recruiters rely on this filter to find passive yet qualified candidates.


2. Treat your headline like SEO + your elevator pitch

Your headline is key real estate- use it to clearly communicate role, expertise, and value.

Weak example:
“Software Developer at XYZ Company”
→ Generic and not searchable.

Strong example:
“ML Engineer | Computer Vision for Autonomous Systems | PyTorch, TensorRT Specialist”
→ Role: ML Engineer
→ Niche: computer vision in autonomous systems
→ Tools: PyTorch, TensorRT

This structure reflects best practices from experts who recommend combining role, specialization, technical skills, and context to stand out.

3. Upgrade your visuals to build trust

→ Use a crisp headshot: natural light, simple background, friendly expression
→ Add a banner that reinforces your brand: you working, speaking, or a tagline with tools/logos

Why it works: Clean visuals increase profile views and instantly project credibility. 


4. Rewrite your “About” section as a human story

Skip the bullet list, tell a narrative in three parts:

→ Intro: “I’m an ML engineer specializing in computer vision models for autonomous systems.”

→ Expertise: “I build end‑to‑end pipelines using PyTorch and TensorRT, optimizing real‑time inference for edge deployment.”

→ Motivation: “I’m passionate about enabling safer autonomy through efficient vision AI, let’s connect if you’re building in that space.”

Why it works: Authentic storytelling creates memorability and emotional resonance   .


5. Be the advocate for your work

Make your profile act like a portfolio, not just a resume.

→ Under each role, add 2–4 bullet points with measurable outcomes and tools (e.g., “Reduced inference latency by 35% using INT8 quantization in TensorRT”)

→ In the Featured section, highlight demos, whitepapers, GitHub repos, or tech talks

Give yourself five intentional profile upgrades this week. Then sit back and watch recruiters start reaching you, even in today’s competitive market.

If you want to be a Data Scientist or AI Engineer in 2025, start here 👇 
This is all you need!

𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀:
📝 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀
 → Advanced ML: Transformers, JEPA, and self-supervised learning
 → AutoML: Automate feature engineering, model tuning, & deployment
 → Data Viz: Build interactive, explainable dashboards
 → Cloud-Native Analytics: Serverless + GPU-accelerated workflows
 → Unstructured Data: Handle multimodal (text+image+audio) inputs
 → Specialized Areas: Federated learning, Responsible AI, Synthetic Data, Time Series Forecasting, Knowledge Graphs

🧰 𝗧𝗼𝗽 𝗧𝗼𝗼𝗹𝘀
 → ML Frameworks: PyTorch, JAX, Scikit-learn, Flax, XGBoost, LightGBM, DeepLearning4j
 → AutoML: H2O.ai, AutoGluon, Auto-sklearn, DataRobot, Fireworks AI Build SDK (for experimentation)
 → Visualization & BI: Plotly, Seaborn, Power BI, Looker, Apache Superset, Grafana
 → Data Platforms: Snowflake, Databricks, Fireworks AI (for high-throughput inference), Apache Spark, Dask, BigQuery
 → GenAI Toolkits: ChatGPT, Claude, Fireworks AI Inference Engine, Hugging Face, Mistral, DeepSeek, LangChain, LLaMA, xAI’s Grok
 → MLOps & Feature Eng.: MLflow, DVC, Kubeflow, Weights & Biases, Flyte, Metaflow, Featuretools
 → Annotation Tools: Label Studio, Snorkel, Prodigy, SuperAnnotate, CVAT

𝗙𝗼𝗿 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀:
📝 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀
 → LLMs: Fine-tuning, supervised + reinforcement-based tuning (Fireworks AI supports both)
 → Agentic AI: Build LangGraph or AutoGPT-style systems with function calling & state management
 → RAG & Retrieval: Design hybrid pipelines with vector DBs & evaluators
 → Scalable Inference: Quantization, compression, distillation
 → Edge & On-Device AI: Optimize models for real-time inference
 → AI Security & Ethics: Understand red teaming, robustness, hallucination detection

🧰 𝗧𝗼𝗽 𝗧𝗼𝗼𝗹𝘀
 → AI Frameworks: TFX, PyTorch Lightning, Hugging Face, FastAI, JAX, Keras
 → Cloud & Compute: AWS SageMaker, Google Vertex AI, Fireworks AI (fine-tuning + inference), CoreWeave, RunPod, Modal
 → GenAI Infra: OpenAI APIs, Claude, Fireworks AI Build SDK, Mistral, LLaMA 3, Ollama, LangChain, LlamaIndex
 → AI Agents: AutoGPT, CrewAI, LangGraph, Haystack, JARVIS, Semantic Kernel, DSPy
 → Deployment Tools: NVIDIA Triton, Ray Serve, BentoML, KServe, ONNX Runtime
 → Dashboards & UIs: Streamlit, Gradio, Voila, Panel, Flask
 → Pipelines & Orchestration: Airflow, Prefect, Dagster, Argo Workflows, Mage AI
 → Optimization & Serving: DeepSpeed, TensorRT, TVM, QAT, Fireworks Inference Engine (for latency-sensitive apps)
 → Security & Privacy: PySyft, CleverHans, Flower, Opacus, Adversarial Robustness Toolbox

Remember: You need to not just keep learning new topics, but you need to apply them with hands-on projects!

--------
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Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources to keep you up-to-date about the AI space!

If I think about my mom’s career, it looked very different from ours today.
She worked at the same company for 30 years.

Back then, loyalty made a lot of sense:
→ Companies provided stability and growth in return.
→ Long-term employees were valued and rewarded.
→ Staying at one place meant deeper relationships, mentorship, and trust.

But the reality has changed.

When I told my mom that in our generation, it’s common to switch companies every 2-3 years, she was honestly surprised.

She asked, “But why?”

Here’s my personal take:

Loyalty to a company only makes sense if your career growth is tied to it. If you’re learning, appreciated, rewarded, and see a path forward, great.

But staying put just because?
That’s not the world we’re in anymore.

We’ve seen how even high-impact employees, with 10+ years at a company, can be let go overnight.
And that’s the harsh reality of today’s market.

This is why I strongly believe in building your own brand and your own skill set, independent of any employer or title.

→ You’re not obliged to a company because they “gave you” a job.
→ You are getting paid for the value you deliver. In many cases, you might be delivering more.
→ Your identity should be built on your capabilities, not your badge.

Here’s what I remind myself often:
If at any point you feel your team or company goals no longer align with your career goals, or if you feel stuck and not growing - don’t hesitate to move.

Switch to a new team.
Join a new company.
Start your own thing.

Don’t get trapped in the comfort zone and look back later wondering why your career didn’t go where you wanted it to.

In the end - you owe it to yourself to keep learning, growing, and shaping the trajectory you want.

#JustMy2Cents

If you’re getting started with your journey in Data Science and AI, here are some of the books you’ll find handy.

I’m a little old school - I still love reading physical books. I keep them around me, mark them up with notes, and come back to them when I need clarity.
Short-form content is great, but when I want to really understand something, I turn to books.

Here are some of the books that helped shape my understanding of ML, AI, and data science - organized by where you are in your learning journey.

📘 Beginner-Level
For those just starting out, these are accessible but foundational.

→ The Elements of Statistical Learning - Hastie, Tibshirani & Friedman
A must-read classic that builds intuition for regression, decision trees, and ensemble methods.

→ Data Science for Business - Provost & Fawcett
Teaches analytical thinking and how data science impacts real business decisions.

→ The Hundred-Page Machine Learning Book - Andriy Burkov
Short, clear, and practical - a great ML overview to read cover to cover.

→ Python for Data Analysis - Wes McKinney
Teaches pandas and NumPy for practical data wrangling and analysis.

→ Practical Statistics for Data Scientists - Bruce & Gedeck
Covers real-world statistical techniques used in DS/ML pipelines.

→ Intro to ML with Python - Andreas Muller & Sarah Guido
A hands-on approach to building ML models using scikit-learn.


📗 Intermediate-Level
If you’re past the basics and ready to build deeper systems or models.

→ Hands-On ML with Scikit-Learn, Keras, and TensorFlow - Aurelien Geron
One of the best end-to-end practical guides to ML and deep learning.

→ Deep Learning with Python - François Chollet
Written by the creator of Keras - great for experimenting with neural nets.

→ Designing Data-Intensive Applications - Martin Kleppmann
A fantastic read on the architecture behind scalable data systems.

→ Data Science from Scratch - Joel Grus
Builds algorithms from first principles - great for foundational understanding.

→ Building ML-Powered Applications - Emmanuel Ameisen
Focuses on deploying ML models that deliver real impact.

📙 Advanced-Level
For those building large-scale systems or diving into research.

→ Deep Learning - Goodfellow, Bengio & Courville
The definitive DL textbook, foundational for understanding neural networks.

→ Reinforcement Learning - Sutton & Barto
The classic RL textbook - if you’re serious about agents, this is where you start.

→ RL with TensorFlow and Ray - Ritwik Sathe
A practical, engineering-first approach to implementing RL at scale.

→ Pattern Recognition and ML - Christopher Bishop
A rigorous statistical treatment of ML from a Bayesian perspective.

→ Probabilistic Graphical Models - Koller & Friedman
For those exploring structured models and probabilistic inference.

→ Mathematics for ML - Deisenroth, Faisal & Ong
Great if you want to sharpen the math that powers ML algorithms.

Happy Reading 📖 
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It’s wild how some of the most meaningful professional relationships start in the most unexpected ways.

Back in 2021, I got an email from someone I didn’t know, but it was so thoughtful, so intentional, and so real that I had to respond.

That someone was Harini. And I’m so glad I did.

I remember thinking, “I need to find a way to work with her.”

And thanks to Aditya for connecting the dots and helping us find common ground, what started with one initiative quickly grew into many. From then till now, she’s been a constant support, a collaborator I deeply trust, and someone whose energy I’ve always matched so effortlessly.

Today, when we sat down, 5 hours flew by like five minutes.

We swapped stories, brainstormed ideas, laughed about our journeys so far, and reflected on just how much we’ve learned, built, and grown along the way.

Harini is not just a brilliant data scientist, she’s one of the kindest, most thoughtful people I’ve had the joy of working with. And it’s rare to find people you connect with so naturally- both personally and professionally.

So grateful for the serendipity of that email, and for all that we’ve created together since.

Looking forward to more in-person catch-ups, more big ideas, and everything in between 💛

If you’re an AI engineer building a full-stack GenAI application, this one’s for you.

The open agentic stack has evolved. It’s no longer just about choosing the “best” foundation model. It’s about designing an interoperable pipeline, from serving to safety- that can scale, adapt, and ship.

Let’s break it down 👇

🧠 1. Foundation Models

Start with open, performant base models.
→ LLaMA 4 Maverick, Mistral‑Next‑22B, Qwen 3 Fusion, DeepSeek‑Coder 33B
These models offer high capability-per-dollar and robust support for multi-turn reasoning, tool use, and fine-grained control.

⚙️ 2. Serving & Fine-Tuning

You can’t scale without efficient inference.
→ vLLM, Text Generation Inference, BentoML for blazing-fast throughput
→ LoRA (PEFT) and Ollama for cost-effective fine-tuning

If you’re not using adapter-based fine-tuning in 2025, you’re overpaying and underperforming.


🧩 3. Memory & Retrieval

RAG isn’t enough, you need persistent agent memory.
→ Mem0, Weaviate, LanceDB, Qdrant support both vector retrieval and structured memory
→ Tools like Marqo and Qdrant simplify dense+metadata retrieval at scale
→ Model Context Protocol (MCP) is quickly becoming the new memory-sharing standard

🤖 4. Orchestration & Agent Frameworks

Multi-agent systems are moving from research to production.
→ LangGraph = workflow-level control
→ AutoGen = goal-driven multi-agent conversations
→ CrewAI = role-based task delegation
→ Flowise + OpenDevin for visual, developer-friendly pipelines

Pick based on agent complexity and latency budget, not popularity.

🛡️ 5. Evaluation & Safety

Don’t ship without it.
→ AgentBench 2025, RAGAS, TruLens for benchmark-grade evals
→ PromptGuard 2, Zeno for dynamic prompt defense and human-in-the-loop observability
→ Safety-first isn’t optional, it’s operationally essential

👩‍💻 My Two Cents for AI Engineers:

If you’re assembling your GenAI stack, here’s what I recommend:

✅ Start with open models like Qwen3 or DeepSeek R1, not just for cost, but because you’ll want to fine-tune and debug them freely

✅ Use vLLM or TGI for inference, and plug in LoRA adapters for rapid iteration

✅ Integrate Mem0 or Zep as your long-term memory layer and implement MCP to allow agents to share memory contextually

✅ Choose LangGraph for orchestration if you’re building structured flows; go with AutoGen or CrewAI for more autonomous agent behavior

✅ Evaluate everything, use AgentBench for capability, RAGAS for RAG quality, and PromptGuard2 for runtime security

The stack is mature. The tools are open. The workflows are real.

This is the best time to go from prototype to production.

-----
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I write deep-dive blogs on Substack, follow along :)
https://lnkd.in/dpBNr6Jg

Headed to Amsterdam 🇳🇱

I’ll be speaking at TNW Conference this week on a topic that’s super close to my heart, Beyond the Model: Building Scalable Agentic Systems with Open Source AI.

Right now, the conversation in AI is moving fast, it’s not just about building bigger models anymore. It’s about building real-world, production-grade AI systems that can reason, act, and drive business value.

In my talk, I’ll be sharing,
→ Why agentic AI is only as good as its production performance
→ How to combine open-weight models with the right inference infrastructure like Fireworks AI, tool protocols (like MCP), and orchestration layers
→ What I’m seeing work in the field today, from architecture patterns to infra choices

If you’re building in open-source AI, I’d love to see you there.

And, if you’re in Amsterdam (whether attending TNW or just in town), I’m also hosting a casual AI Coffee Meetup ☕ at STRAAT Café (walking distance from the venue) on June 19th at 3PM CEST.

No agenda, no talks, just good coffee and great conversations with fellow AI engineers, builders, researchers, and anyone curious about this space.

If you’d like to join, please fill out this quick form so I know who’s coming 👉 https://lnkd.in/d25yTNkq

Amidst all the negative press the U.S. is getting lately, I wanted to take a moment to share my perspective on how things really are.

In my opinion, the last seven years that I’ve been here, I’ve not only been able to achieve things I once dreamt of- but more. 

I’ve been given opportunities that I couldn’t have imagined when I first moved here in my early 20s. For me, it’s truly been the American dream. And I know it has been for so many others too.

This country has been incredibly welcoming to me. I lived in New York for four years, and the last three in the Bay Area; and never once have I felt like a foreigner. 

Whether it’s been at work, in the community, or while traveling across the country, I’ve felt seen, included, and supported.

The culture here is one of the most open I’ve experienced. People are treated with respect, regardless of where they come from, their age, or gender. I’ve never felt like I had to “earn” the right to be heard, people here genuinely listen, and they’re willing to give you a chance based on your potential.

At IBM, Microsoft, Google, and now Fireworks AI- I’ve worked with people who believed in me before I even fully believed in myself. Every team I’ve been part of has invested in my growth and treated me like an equal. That mindset of “let’s grow together” is something I’ll always be grateful for. (Most people with some exceptions- not important for this post)

And while I do believe the U.S. immigration system still has a long way to go, and I’m not denying the challenges that many face because of it.

This post is not about the system. It’s about the PEOPLE. The mentors, the teammates, the community. It’s about the culture that values initiative, rewards merit, and gives people space to dream and build.

So to every student who’s been reaching out and wondering, “Is it still worth coming to the U.S.?” my honest answer is YES.

It’s not perfect. But no place is.

This is still a land of opportunity. A place where dreams can take shape. A place where people will cheer for you, even when you’re just getting started.

I truly hope things continue to improve, for those here, and for those planning to come. I hope every student and early-career professional gets the same opportunities I was fortunate to have. 

And I hope the American dream continues to be real- not just for me, but for all of us.

Right after my keynote at TNW, I hosted a small meetup in Amsterdam.

I wasn’t sure what to expect.
But a solid group showed up both familiar faces and new ones.

We had a couple hours of detailed, honest conversations:
– How people are applying agentic AI today
– Why building a personal brand still matters in an age of automation
– And what makes the EU AI ecosystem unique (I learned a lot)

It was a reminder:

→ You don’t need a 100-person event to spark real insights
→ Just a few people who care about the same things

This gave me the energy to do more.
I’ll be hosting meetups like this in more cities soon.

It still blows my mind how easily you can build a global network today with just LinkedIn and a bit of intent.

Thanks to everyone who came out.
Dr. Seth Dobrin, Sai Anand Nagarajan, Aditya Suresh, Dev Jadhav, Kirill Solodskih, Solodskikh , Parul Benien, Suvid Velayudhan, Bruno Pais, Jaap Gielink 

Where would you like to see a meetup next?

PS: I do AMAs, IG Lives, and share a lot more personal insights and tidbits on my IG page. We should plan more in-person meetups. 

❤️If you haven’t been following me there yet, I’d love for you to join: https://lnkd.in/denE_Zpw

One of our toxic traits as millennials and Gen-Z is that we often associate success with money. 

In many of my mentoring sessions, I have seen people trying to align their career trajectory with what will help them make most money in the next 1-2 years, without thinking about what are they really passionate about, what skillset they want to build or question the impact they want to make. 

Read, and read again. Money is a by-product of success! Money != Success

Living in bay-area I experience this much more. Obviously this is one of the highest cost-of-living locations in the entire country, but believing money is directly proportional to success is really toxic, and will hurt your mental health. Trust me I have seen too many such cases to confirm it.

The single-minded chase towards money might get you instant gratification, but most likely will not put you in the path of long-term success. One of the common examples of what people do is, switching jobs just because the next job pays you 50-70k more. 

What you should do instead when switching jobs is ask yourself "Does this job switch take me closer to what I envision is my career path for the next decade?", "Does it challenge me to build new skills that I want to work on?", "Does this new role give me an opportunity to work on a problem and create the impact I am looking to make?"

The way I look at success is:
- Am I waking up motivated?
- Do I have a happy personal/family life?
- Do I feel content about where I am with my life and career?
- Have I been growing my skills and become a better version than what I was a year ago?
- Are you making enough money to pay for your needs and top-priority wants?

Is your answer is yes to all of these, then my friend you are pretty successful 😄

As you navigate your paths, shift your focus from the ephemeral satisfaction of immediate rewards to lasting fulfillment of realizing your deepest passion and potential. 

PS: As a data scientist, I would say stop looking for local maximas and instead search for global maxima 🤓

Most people overestimate what they can do in a week… But they dramatically underestimate what a decade of focused self-investment can do.

Ten years. Of showing up. Of choosing growth over comfort. Of doing the quiet work when no one’s watching.

That’s the difference between being who you are, and becoming who you’re meant to be.

Here’s what I’ve learned:

🧠 Your brain is still becoming. Use that.
The part of your brain responsible for strategy, foresight, and discipline, the prefrontal cortex, keeps developing well into your twenties. But here’s the truth: it keeps evolving with intentional effort.

You are not stuck. You’re not fixed.
The more you teach your mind discipline, focus, and clarity… the better it performs for you.

📖 Your habits are writing your story
Every single day, you’re casting a vote for the kind of person you want to become.

Not through massive breakthroughs, but through small, consistent behaviors.
The way you spend your mornings.
The thoughts you feed.
The commitments you keep, especially the ones no one else sees.

This is where identity is forged. Not in ambition, but in discipline.

🔍 Your self-awareness is your power
When you get quiet enough to observe your own patterns, your emotional triggers, the beliefs you inherited, the stories you’re repeating, you give yourself a choice:

👈 To react from your past,
or to respond from your future 👉 

This kind of reflection isn’t soft work. It’s the hardest, most necessary work.

🚀 You’re not a victim of life. You’re the designer of it.
Everything changes the moment you realize:
You’re not waiting to be chosen.
You’re choosing yourself, over and over again- and that choice COMPOUNDS!

🎯 Audacious goals aren’t unrealistic. They’re under-scheduled.
Your dream life needs structure.
Visualize it clearly. Then break it down.
Not into to-do lists, but into systems.
Build feedback loops. Track tiny wins. Reward effort. Iterate quickly.

You don’t need to leap.
You just need to keep moving- consistently, intentionally, daily.

🌱 And you can’t do it alone.
Your environment is not neutral.
Mentors, partners, and people with original minds will either accelerate or decelerate your growth.
You need mirrors, the kind that reflect your potential, not your fears.
Find them. Keep them close.

If you give one decade your full self, your effort, your curiosity, your honesty, your resilience, trust me → It will give you back a version of yourself you couldn’t have imagined at the start.

That’s the kind of ROI no investment beats.

I’ve shared more of my personal journey and mindset shifts like this in my book, you can check it out here if you're curious: whatsyourworthbook.com

I never set out to build a career in AI. As a kid, I was covered in paint splatters and glued to craft projects. I thought I’d grow up to be an artist. There’s a story behind why I never pursued art professionally (maybe I’ll share that one day).

Then, in middle school, I discovered the PC Logo tool 🐢 (IYKYK): making crazy patterns with LT and RT commands, felt just as magical as painting. 

That creative spark led me to notice something else: I was inherently drawn to physics and math, balancing equations and unraveling logical puzzles- while chemistry and biology never held my attention. My brain craved analytical challenges, and every time I debugged a code or solved a tricky math problems, I felt that same thrill I once got from painting.

One of my mentors put it perfectly: “Your superpower lies in what sets you apart. With limited time that all of us have, channel your energy into what makes you unique.” For me, that meant leaning into my love for math and computational thinking, and the joy of discovering how machines learn.

Here’s how those pieces came together, and how they might help you find your own path:

→ Embrace your first sparks. Remember the activities that lit you up, whether that was painting watercolor landscapes or guiding a Logo turtle to draw circles.

 → Notice your natural inclinations. Which subjects or tasks pull you in? For me, physics equations and algorithmic puzzles were irresistible; biology diagrams, not so much.

 → Celebrate small wins. Every time you solve a problem, automate a task, or complete a creative project, you build confidence, and momentum.

 → Lean into what makes you unique. Your blend of strengths, like creativity plus analytics, or storytelling plus data, can become your superpower.

 → Keep experimenting. Passion often emerges at the intersection of unexpected interests. Try new tools, challenge yourself, and pay attention to where you lose track of time.

Your journey won’t look like anyone else’s- and THAT'S the point. Whether you’re mixing paints or writing your first lines of code, those early curiosities can point you toward work you love. ❤️

If you’re preparing for a Data Science role, here is a roadmap that will help you 👇

The expectations in 2025 are different. It’s not just about knowing models - it’s about systems, business value, and being production-ready. 

Here’s what to focus on:

1. Stay Current with the Latest Frameworks & Tools

→Deep Learning: PyTorch 2.x, TensorFlow 2.x, JAX
→MLOps & Data Engineering: Kubeflow / MLflow, Apache Airflow & Spark, Ray
→ Generative AI & LLM: Hugging Face Transformers and other opensource models, LangChain, LlamaIndex, AutoGen, CrewAI, LangGraph

💻 Resources: 
↠ Fast.ai Course: https://course.fast.ai/
↠ Kubeflow Docs: https://lnkd.in/dw6pr5E4
↠ Hugging Face Tutorials: https://lnkd.in/dgj7NJHT
↠ Ray Docs: https://lnkd.in/dZ-iyzrd 

2. Understand the Typical Interview Rounds

⦿ Initial Technical Screen
→ Coding Challenge: Python (or R/SQL) focusing on data manipulation, algorithms, or quick problem-solving.
→ Statistical & Probability Foundations: Expect questions on hypothesis testing, confidence intervals, A/B testing, Bayesian inference.

⦿ ML & Analytics Case Study
→ Data Analytics: Exploring real-world scenarios (e.g., user retention at a streaming company).
→ Machine Learning Model Discussion: How would you approach feature engineering, model selection, and evaluation?

⦿ ML System Design
→ Architecting Scalable Pipelines: Data ingestion, model training, evaluation, deployment, and monitoring.
→ Trade-offs: Discuss latency, real-time vs. batch predictions, cost-effectiveness, and reliability.

⦿ Behavioral & Team Fit 
→ Past Projects: Deep dives into your approach, impact, and lessons learned.
→ Collaboration & Communication: How you handle feedback, lead cross-functional teams, and present insights.

💻 Resources:
↠ ML System Design by Alex Xu: https://amzn.to/4bdsjUR
↠ LeetCode: https://leetcode.com/
↠ StrataScratch: https://lnkd.in/dpJg9cKf

3. Leverage a Business Value Framework

→Objective: Identify key metrics (e.g., revenue, user retention).
→Feasibility: Data readiness, engineering constraints.
→ROI: Quantify impact; balance complexity vs. gains.
→Trade-offs: Speed vs. quality, cost vs. performance.

💻 Read: https://lnkd.in/durKR-uP

4. Tackle Case Studies with Structure
→ Clarify the Problem: Scope, business goals, data needs.
→ Identify Key Metrics: Conversion, churn, retention.
→ Propose a Solution: Data pipeline, model type, validation.
→ Discuss Pitfalls: Data quality, ethical issues, costs.

5. ML System Design & Tools
→ Data Ingestion & Processing: Apache Airflow, Apache Spark, Kafka
→ Model Development & Experimentation: PyTorch, TensorFlow, Hugging Face
→ MLOps: MLflow, Weights & Biases, Kubeflow, DVC
→ Deployment & Inference: Fireworks AI, Hugging Face Endpoints, AWS SageMaker, GCP Vertex AI, Docker/Kubernetes
→ Monitoring & Maintenance: Prometheus/Grafana, Evidently AI, Arize AI, Seldon Core

If you’ve ever thought angel investing is only for rich people in closed-door circles, you’re not wrong!

The first time I invested, it wasn’t through a syndicate or formal program. It was through friends, people I trusted who were building something meaningful and needed early believers, not big checks.

That’s when it clicked: you don’t need millions to be an angel investor. You need conviction, curiosity, and the willingness to learn.

Later, I joined Angel Squad by Hustle Fund, and that helped me formalize my approach, giving me access to vetted deals, shared evaluations, and a network of other investors.

Here’s the 5-step roadmap that helped me start, and can help you too:

1. Start with what (and who) you know
My first few investments were into startups founded by people I knew personally.
They weren’t raising huge rounds, just looking for people who understood their vision.
→ Even a $1K–$2.5K check counts
→ What matters is: do you believe in the founder, and can you add value?


2. Invest in what you understand
At the early stage, things change, products pivot, markets shift.
→ Focus on sectors where you understand the problem deeply
→ Ask: do I believe this founder can adapt when things get hard?
In the beginning, don’t chase hype, bet on founders who are resilient and sharp.

3. Learn the basics of angel investing
You don’t need an MBA, but you do need to understand:
→ SAFE vs Convertible Notes
→ Cap tables and dilution
→ Pre-money vs Post-money valuation
→ Pro-rata rights and exit paths
Great resources:
→ Angel Investing School
→ YC’s SAFE Docs
→ AngelList Glossary
→ YC’s Startup School

4. Join a community and learn with others
Joining Angel Squad helped me learn how experienced angels evaluate deals.
You don’t have to do this alone, great communities include:
→ Angel Squad
→ On Deck Angels
→ AngelList Syndicates
→ VC Starter Kit
These networks help you learn faster and access stronger deal flow.

5. Start small, stay curious, and add value
You don’t need deep pockets, just thoughtful conviction.
→ Start with small checks you’re comfortable with
→ Track your decisions and ask for updates
→ If you can, offer your domain knowledge, advising shows you’re invested beyond capital

And one final note, angel investing is a long-term game.

If you’re expecting quick returns, this probably isn’t for you. These are illiquid, high-risk bets that take years to play out.

But if you believe in the builders, and you’re excited to help shape early-stage ideas, there’s real joy in that.

Always happy to chat or share resources if you’re exploring this path.

If you’re AI-curious but can’t decide where to start, this one’s for you 👇

The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering:

👉 Should I become a Data Scientist, ML Engineer, or Product Manager?

Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle.

📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge).


🚀 The 10 Career Paths in AI, Simplified:

➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier
➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation
➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them
➡️ Data Engineer – building the infrastructure to move and manage data
➡️ Software Engineer – writing core product code with ML components
➡️ Data Analyst – analyzing data to drive insights and business impact
➡️ BI Analyst – working with KPIs, reporting, and decision frameworks
➡️ AI Consultant – advising teams and clients on adopting AI responsibly
➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals
➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions


🧭 How to choose the right one for you:

→ Start with your natural strengths: coding, communication, business thinking, or data sense

→ Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate

→ Stack the right skills intentionally:
	•	Coders: Python, PyTorch, prompt design, eval frameworks
	•	Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs
	•	Insights: Analytics, causal reasoning, dashboard tools
	•	Translators: AI roadmap building, governance, storytelling

→ Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments

→ Develop a T-shaped skill profile – go deep in one role, but stay conversational across others


💡 A few truths to keep in mind:
→ You don’t need to be a “10x coder” to work in AI
→ Problem-solving > job titles
→ Projects > perfect resumes
→ Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way
→ There’s no “entry-level” in AI – just entry-level impact


📖 Curious to explore deeper?
Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

Just got back from Snowflake Summit, and it sparked some important reflections on where AI is really headed in production.

One theme came through loud and clear: AI is no longer a side project, it is becoming part of the operational backbone of modern enterprises.

A few shifts I noticed:

→ From data-first to AI-first thinking
We're seeing AI being used upstream - to prepare, contextualize, and govern data before it drives downstream actions. Semantic layers and agent orchestration are now essential tools.

→ Agentic AI is moving past the hype cycle
The real conversations were about architecture - memory management, feedback loops, and designing for safety. It is encouraging to see the focus moving to engineering grounded, reliable agents, not just demos.

→ Governance is front and center
Lineage, explainability, and trust must be integral at every layer. Building GenAI systems without this foundation will not scale safely.

What stood out most was Snowflake’s pragmatic approach - building the rails for robust, governed GenAI, grounded in real-world enterprise needs.

Some of my favorite moments:

❄️ Seeing Canva enhance creativity at scale with AI

❄️ Watching multi-modal complaint data processed live in Cortex

❄️ Hearing business users ask fuzzy questions, and get explainable answers in Snowflake Intelligence


This Summit reinforced a core truth: it is not about chasing model benchmarks or flashy UX alone. It is about building AI that actually works - reliably, safely, and at scale. 

#SnowflakePartner #SnowflakeSummit

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