Armand Ruiz

Director Data Science & AI Elite

Leading the Product Management org for the AI Portfolio of products focused on Generative AI and Traditional ML. Products include: watsonx.ai and watsonx.gov

Vice President of AI Platform

I am building the world's largest team of 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 by

- Recruiting hundreds of new engineers around the globe
- Reskilling our existing talent
- Offering them top-quality training in new-generation AI

Responsible for Pre-Sales Generative AI Pilots with the new IBM watsonx.ai Platform.

Director watsonx team - Client Engineering

Director of the group IBM Data Science Elite team. We prove the value of IBM Watson solutions by co-creating pilots with our customers. I managed a team of 90+ data scientists that worked on 100s of complex AI implementations.

Director Data Science

I manage a rockstar team of Product Managers leading the product portfolio of IBM Watson AI products.

Product Director - Product Management, Watson Studio and Machine Learning

Leading new Data Science products for IBM

Lead Product Manager - IBM Data Science and Watson Machine Learning

Bring Open Source data science tech to IBM stack

Product Manager for Data Science Open Source Extensibility 

In charge of Data Science projects in EMEA

Data Science Advocate - IBM Data Science & Machine Learning

Support sales in Data Science opportunities

Technical Seller Data Science & Smarter Cities

Internship to bring new innovations to IBM

Intern - Technical Software Engineer, Extreme Blue

Vodafone

Optimize 3G network coverage using Data Science

Data scientist - Radio Optimization Engineering

Federació Catalana de Tennis

Tennis Coach

VP of Product - AI Platform @IBM

As the Director of AI Engineering at IBM, I lead the watsonx team, a global group of over 1,000 AI Engineers who work on cutting-edge Generative AI solutions for various industries. With more than 12 years of experience in AI, I have a passion for democratizing practical AI skills and empowering professionals to leverage AI to transform their business.

Previously, I managed IBM's Data Science and AI Elite, delivering complex AI implementations for hundreds of clients. 

I also led the product management of IBM Watson Studio and Watson Machine Learning, an AI platform used by hundreds of customers and SaaS apps powered by AI. I hold a Master of Science in Telecommunications from Universitat Politècnica de Catalunya and speak three languages: Spanish, Catalan, French, and English.

Connect with me!

✉️ JOIN MY NEWSLETTER: Every Saturday morning, you'll get 1 actionable tip to launch and grow your AI strategies in business in less than 4 minutes. → GO HERE: https://www.nocode.ai/

📧 Email: armand@nocode.ai
X: @armand_ruiz


"It is kind of fun to do the impossible"
All opinions are my own and don't represent my employer.

Called it. 🎯 The Model Context Protocol (MCP) is officially moving to the Linux Foundation under the new Agentic AI Foundation.

I’ve been saying for a while that open governance is the only viable path forward for AI infrastructure. Today’s news proves it.

This brings Anthropic, OpenAI, Microsoft, Google, and others under one neutral banner. 

10,000+ active servers. 
97M+ SDK downloads. 
Zero vendor lock-in.

If you want an ecosystem to flourish, you have to open it up. The era of Agentic AI just got a serious upgrade.

Read the news here: https://lnkd.in/g7vy5-x2

At NeurIPS2025, Google presented Titans, a new architecture that combines the speed of RNNs with the performance of Transformers. 

The trade-off between infinite context and computational cost is the biggest hurdle in AI right now.

While Transformers are accurate but expensive to scale, and linear models are fast but lose detail, Titans delivers the best of both worlds by introducing Test-Time Memorization.

The Core Innovation: Instead of a static state, Titans uses a deep neural memory module that actively updates while running, utilizing a "Surprise Metric":
- Low Surprise: Routine data is processed efficiently without clogging memory.
- High Surprise: Anomalies and key details are instantly integrated into long-term memory.

The Results:
- Scales effectively to 2M+ token context windows.
- Outperforms larger models (including GPT-4) on extreme long-context reasoning benchmarks.
- Generalizes beyond text to genomics and time-series.

This is a move from passive processing to active, efficient learning.

Jensen just dropped a masterclass on the future of AI. 

It was great to hear from him in a long-format podcast conversation. 

Three key insights he shared:

1/ Human language is the new syntax: The "tech divide" is closing. You won't need C++ to code the future; you just need to speak.

2/ The "30-Day" Mindset: Nvidia operates like they are always 30 days from bankruptcy. This paranoia fuels their innovation and resilience.

3/ Safety is an engineering problem: Forget the sci-fi movie plots. AI safety is just like cybersecurity—as capabilities rise, so do the guardrails.

Pic taken during my epic commute

No alternative text description for this image

Current mood: "Joining a new company is akin to an organ transplant - and you're the new organ. If you're not thoughtful in adapting to the new situation, you could end up being attacked by the organizational immune system and rejected."

Learning fast, exploring the culture, and pushing myself to bring out the best work I’ve ever done. 

Loving the challenge. 🤩

As a leader, your job is to run a tight ship.

That doesn’t mean control. It means precision.

Clear goals. 
Clear roles. 
Clear standards.

In fast-moving teams, especially in AI, ambiguity is expensive and confusion kills momentum.  Alignment compounds it.

Gemini 3 is here and it isn’t just another model release. It’s Google firing on every AI cylinder: infra, research, product, and distribution.

A few things stand out:

1.	Full-stack advantage, finally realized.
Gemini 3 is the first model shipped into Google Search on day one. That’s not just marketing ; it’s how you put frontier AI in front of billions.

2.	The agent era is no longer theoretical.
With Deep Think mode, long context, multimodal understanding, and real-time planning, Gemini 3 moves beyond chat. It’s not a chatbot. It’s a problem-solver.

3.	AI is getting better at “reading the room.”
It reflects a core shift: models are starting to grasp intent and nuance and not just data.

Benchmarks back it up: topping LMArena, solving “Humanity’s Last Exam”, setting a new bar in math and science tasks. But that’s the sideshow.

More info here: https://lnkd.in/gWeXGwhC

I cannot wait to try it out

JSESSIONID

li_at

Talks about #ai, #nocode, #deeplearning, #machinelearning, and #artificialintelligence

building AI systems

𝗔𝗜 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 𝟭𝟬𝟭

Current AI is often described as a "statistical correlation machine." It predicts the next word or pixel based on patterns it has seen before. World Models represent a shift toward causal understanding—giving machines a way to simulate the future.

What is a World Model? Think of it as an internal flight simulator. Instead of just recognizing an environment, the AI builds a mental model of how that environment works. It uses this model to test hypotheses ("If I drop this glass, it will break") without needing to actually perform the action.

The "𝗟𝗮𝘂𝗻𝗱𝗿𝘆" Example Imagine training a robot to fold clothes.

- 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot must physically interact with the clothes millions of times, learning through endless trial and error.

- 𝗪𝗶𝘁𝗵 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot simulates the physics of the fabric in its head. It plans the fold internally, predicts the outcome, and only moves its arm when it is confident. This results in safer, faster, and more generalizable learning.

𝗛𝗼𝘄 𝘁𝗵𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗟𝗠𝘀: While Large Language Models (LLMs) are masters of syntax and semantics, they often struggle with physical causality. World Models differ because:

𝟭. 𝗧𝗵𝗲𝘆 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗰𝗮𝗹𝗹: They don't just retrieve information; they run mental experiments.

𝟮. 𝗧𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻 𝗹𝗮𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗹𝗲𝘀: They infer the physics of their environment from data rather than being hand-coded.

𝟯. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲: They predict "what happens next" in a state-space, not just a token-space.

𝗧𝗵𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: The next frontier is the convergence of these technologies. By combining the reasoning of LLMs with the grounding of World Models, we move closer to Embodied Intelligence—software that can read, watch, plan, and act in the physical world.

Scaling laws in AI are nowhere near finished. The latest Gemini 3 results show it clearly.

Pre-training: Many believed progress had plateaued. Yet the jump from 2.5 to 3.0 is one of the biggest leaps we have seen. When you improve data, architecture, and compute together, you keep breaking ceilings. No walls in sight.

Post-training: This might be the real frontier. We are still early in how we align, steer, and specialize models. The innovation curve here is just beginning, and the gains on 3.0 prove it.

Good insights from Deepmind engineers this week.

Better models are not coming slowly. They are coming faster.

Plan for continuous capability jumps, not incremental upgrades. The disruption curve is steepening.

it is very cool to work everyday with people in person. i think  product innovation and magic really happens with real human interaction

AI is making 10X engineers 1000X, and 1X engineers 0.1X 

stealing this quote from Shadi Copty 🙃

Fortusight, Founder | AI advisor | Ex-Dir of Llama @Meta & Ex-VP of Watson @IBM

AI Evals Explainer

Generated with Nano Banana Pro

It’s insane how many hours I’ve seen senior technical leaders burn on architecture charts. 

All that time spent polishing pixels... now you just describe it in plain English and boom and nano banana AI handles the rest.

By the way, this is a solid, modern and agile AI stack. To build at x10 speed you need to have the right building blocks.

Fortusight's AI stack

A lot of people ask me this question, I'll start with the absolute core:
1. I use Agno for multi-agent framework, they're fast, elegant and robust.
2. Exa for web grounding, I consider Exa a bigger booster than LLM choice for my use case.
3. FMP for premium, clean, reliable financial data turned into tailored tools.

For LLMs, I use O3 for multi-agent orchestration and planning, GPT 4.1 for digesting web content and supporting my A1Facts flow; Flash2.5 for structured extraction and Gemini 3 pro for synthesizing investment thesis.

I constantly progress the choices of LLMs prioritizing quality over price.

For tools, I use Cursor with Opus 4.5 (recently replacing Gemini 2.5 pro); and Figma make - which I absolutely love.

For internal ops agents, (1) managing coverage requests (2) digesting user feedback (3) analyzing product analytics (4) writing my weekly newsletter -- all built with Agno and Gemini 3 pro.

Less exciting but just as critical :-) Streamlit for front end [being replaced with react], and Firebase for database/auth/analytics -- both choices have been really critical for prototyping rapidly with my users.

The "secret sauce" is secret (lol), but I will say solving factuality in evolving market dynamics is a significant effort, and using expert-system techniques and simulations augments LLMs where they are critically weak.

Happy to share more if you have any questions -- and obviously here's the link if you want to try it out (https://lnkd.in/g7eR86dr)

Introducing SAM 3D: Powerful 3D Reconstruction for Physical World Images

and it’s not your typical 3D reconstruction tool.

It does what previous models couldn’t:
- Reconstruct real-world objects and scenes from a single image.
- Handle occlusion, indirect views, and cluttered backgrounds.
- Estimate human pose and shape with surprising accuracy.

Why does this matter?
Because for the first time, we’re seeing 3D perception at the scale, quality, and accessibility of today’s 2D models.

The architecture behind SAM 3D borrows from LLMs: pre-training on synthetic data, followed by post-training on real-world images using a human-in-the-loop ranking engine. The result is a feedback loop that continuously improves both the data and the model.

The implications stretch far beyond creative media. Robotics. E-commerce. Sports medicine. Interactive avatars. You name it.

And the best part? 

It's fast. 
Real-time fast.

Meta’s already using SAM 3D to power a “View in Room” feature in Facebook Marketplace; turning static listings into immersive experiences.

The gap between virtual and physical is shrinking. SAM 3D is a serious leap forward.

Learn more:
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗯𝗹𝗼𝗴: https://lnkd.in/g8w6dvAB
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gd_HQE9c
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gp3p9jaf
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀: https://lnkd.in/gfwmcKGK
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆: https://lnkd.in/g9CPHEXJ
- 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: https://lnkd.in/guSDTnU3

A sharp, modern AI tech stack for building a serious startup.

Fortusight from Shadi Copty lays out a blueprint that actually reflects where the industry is going:

multi-agent architectures, real-time grounding, premium data, and a diversified LLM strategy tuned to specific jobs rather than brand loyalty.

1/ Agno for agent frameworks.
2/ Exa for grounding.
3/  FMP for clean financial data.
4/ A portfolio of models (O3, GPT-4.1, Flash2.5, Gemini 3) each picked for its superpower.

Cursor + Opus 4.5, Figma Make, Firebase, and Streamlit/React for fast product loops.

And a layer of expert systems + simulation to fix factuality where LLMs keep failing.

This is the kind of stack that lets a small team punch far above its weight.

🚀

Open article: Financial Analysis App by fortusight.streamlit.app

Exciting project for you all to check! I tried an earlier prototype and it was very impressive

Startup life, here I come :-)

Thrilled to share with you my new adventure, Fortusight - a project I've labored on nights and weekends for the last 2 years.

It's a tool for the serious investor that wants to deeply understand a company before buying and holding for 5-10 years.

It uses AI Agents to read through filings, earning calls, analyst reports, news, press-releases - forms an opinion on a company's competitive position, management's friendliness to shareholders, and creates bull/base/bear scenarios for valuation.

To quote a friendly hedge fund manager "I used to have 5 people doing all of this" - each company is analyzed for 192 different perspectives, with A1Facts to ensure factuality for high stakes decisions.

Here's how you try it: https://lnkd.in/g7eR86dr

Let's go 🚀 🚀 🚀

🚨 Stop scrolling. The math on this is barely real.

Get a $799/mo institutional AI research platform for just $50/mo.

The catch: Only 10 spots open. 

RIAs & Fund Managers—this is a complete no-brainer. 👇

https://fortusight.ai/

I need your help 🙏

I'm looking for design partners for my baby startup that just got pre-seed funded.

If you know a wealth fund manager, a small RIA, a family office that wants to reduce their equity research costs from ~$75K to less than what they pay for starbucks coffee - share this link with them: https://fortusight.ai  

For 10 design partners, I'm giving away Fortusight for $50/month instead of $800/month for a year in exchange for feedback to help guide the product's development.

The beta with our users has been incredibly successful, with recurring usage of our equity deep dives.

This will propel the company forward, and one day I will find ways to repay your kindness - many thanks ❤️

Gradient Ascent

Gradient Ascent is the go-to AI newsletter trusted by thousands of researchers, engineers, and managers from top tech firms like Google, Meta, Microsoft, and Amazon, as well as top universities and research labs from over 125 countries worldwide.

Author

Valeo

• Lead a team of 15+ engineers in developing cutting-edge perception R&D solutions for autonomous vehicles.
• Drive innovation, resulting in research papers, patents and delivering scalable solutions to a global network of automobile OEMs.
• Mentor and empower my engineers, fostering a culture of growth, innovation, and technical excellence.

Perception & AI R&D Manager

Intel Corporation

Deep Learning algorithm research and development specializing in Computer Vision, NLP, and Multimodal models.

Previously: Deep Learning algorithm research and development for the NNP line of AI processors

AI/ML Research Scientist

Frontier Development Lab

Mentor and ML lead for the 4pi EUV Solar team

Machine Learning Lead / Mentor

Mentor and ML lead for the Solar Wind project in the NASA FDL 2021 Program

Mentor and ML lead for the Starspots project in the NASA FDL 2020 Program

Qualcomm

Worked in an R&D team, focusing primarily on the use of Computer vision and machine learning in multimedia applications. Focused on Face Pose and Gaze Detection, Synthetic Data Generation, Gesture Recognition, 3D Reconstruction, Object Tracking and development of Qualcomm's FastCV Library

Senior Machine Learning Engineer

University of Michigan

Research on understanding the structure of the environment to enable an intelligent agent to navigate and interact with it.

GSRA

Worked as an intern in the Multimedia R&D team. Focused on developing an algorithm to recognize hand gestures in real time.

Interim Engineering Intern

Ranked by Favikon as:
↳ #1 AI Creator on LinkedIn India 
↳ Top 10 AI Creators on LinkedIn Globally 

Featured in Forbes and Wired

For 14 years, I've been obsessed with a single question: how do we teach machines to truly see and understand our world?

This quest began at Qualcomm. My 3D reconstruction technology landed in Forbes, and the object tracker I co-designed was used in millions of mobile devices. When someone casually used my work in their palm, I glimpsed what's possible when research meets reality.

At Intel Labs, I immersed myself in creating AI systems that bridge the gap between theoretical potential and practical performance—systems that could deliver sophisticated intelligence within real-world constraints. Today at Valeo, I lead a team of AI experts developing computer vision for autonomous vehicles. We're teaching cars to go beyond simple object detection toward true scene understanding. Each breakthrough is a step toward fundamentally transforming how people and goods move worldwide. We are reimagining transportation.

Since 2019, I've guided teams at NASA's Frontier Development Lab as their Machine Learning Lead. We've tackled fundamental questions about our Sun and understanding it better. Our breakthroughs have since been published in conferences like NeurIPS. 

Along the way, I saw a problem I couldn't ignore.

I watched brilliant scientists—experts in physics and natural phenomena—blocked from leveraging AI. Not by ability. But by clarity. Incredible AI research remained locked behind jargon. 

There had to be a better way to get the message across. So, Gradient Ascent was born. In it, I break down complex AI concepts for engineers, product leaders, CEOs, and investors making critical decisions shaping our future. Their response has been humbling.

Outside the lab, I'm an award-winning nature photographer and enjoy illustration and music. These pursuits fuel my creative approach to problem-solving.

✍️ Subscribe to my newsletter for insights that transform technical AI jargon into actionable understanding: newsletter.artofsaience.com

———
Note: All opinions are my own and do not reflect the opinions of my employer

Sairam Sundaresan

Forget $30k bootcamps.
IBM’s 34 free videos will build your AI foundation.

📚 Here's what's covered in this playlist:

1️⃣ Core AI Architectures
→ CNNs, GANs, Transformers, LSTMs, Autoencoders
→ How each works and when to use them

2️⃣ Language Models
→ LLMs, Zero Shot Reasoning
→ NLP vs NLU vs NLG
→ Build RAG systems with clarity

3️⃣ Training & Optimization
→ Backpropagation, gradient descent
→ Overfitting vs underfitting
→ Includes federated learning for privacy-aware systems

4️⃣ ML Fundamentals
→ Supervised vs unsupervised learning
→ Time series, Monte Carlo
→ Core patterns to solve real-world ML problems

5️⃣ MLOps
→ PyTorch inference, embedded + edge AI
→ Deploy and scale AI systems

6️⃣ Agentic AI
→ AI Agents, LRMs, LLM as a Judge
→ Covers AI bias & trust

🎯 Why this matters:
• Debug ML issues with confidence
• Build real AI, not just prompts
• Deploy models that work

🎥 Watch the full IBM AI playlist (34 videos):
🔗 https://lnkd.in/gwypBAMy

♻️ Repost to help someone learn AI the right way.
➕ Follow me, Sairam, for AI engineering that works.
-----
Want to go deeper? Gradient Ascent has you covered. Join 24k+ readers from Google, Meta, Netflix, and over 160+ countries worldwide: https://lnkd.in/gZbZAeQW

The easiest way to learn the basics of AI is this: https://lnkd.in/gTQyc_fi

AI Engineering Leader | Author of AI for the Rest of Us | I help engineers land AI roles and companies build valuable products

Everyone’s building "agents" in 2025
But most are still just wrappers
No memory. No plan. Just prompts

That’s not an agent. 
That’s a chatbot.

The real frontier? 

Feedback loops. 
Orchestration. 
Dynamic systems.

Here's how the landscape of agents has evolved:

🔸 1. Stateless Agents
↳ Fast, schema-bound, API-based
⚙️ Tech: Foundation models, function-calling, structured prompting
📊 Metrics: Latency, schema fidelity
🛑 Risks: Prompt brittleness, semantic drift
🛡️ Fix: Canonical prompts, schema validators, adversarial tests
💡 Examples: GPT-4 Turbo API, Claude Instant (short-context mode), OpenRouter, Ollama

🔸 2. Stateful, Context-Aware Agents
↳ Knows what came before. Personalizes and adapts
⚙️ Tech: Long-context LLMs, memory modules, retrieval caches
📊 Metrics: Recall accuracy, personalization fidelity
🛑 Risks: Memory drift, context corruption
🛡️ Fix: Memory pruning, embedding recalibration
💡 Examples: ChatGPT with Memory, LangChain Memory, MemGPT (research)

🔸 3. Planners
↳ Sequences multi-step tasks across tools or APIs
⚙️ Tech: Multi-step chains, policy-gradient agents, symbolic hybrids
📊 Metrics: Plan efficiency, task completion
🛑 Risks: Tool misuse, loops, misaligned goals
🛡️ Fix: Reward audits, hierarchical goal validation
💡 Examples: AutoGPT, CAMEL agents, OpenAI o1 reasoning-model family, Graph-of-Thoughts

🔸 4. Learners
↳ Adapts over time. Improves with feedback
⚙️ Tech: Meta-learning, online adaptation, RLHF
📊 Metrics: Reward slope, adaptation speed
🛑 Risks: Catastrophic forgetting, instability
🛡️ Fix: Replay buffers, regularized retraining
💡 Examples: LoRA fine-tuning from feedback, AlphaZero, DPO-tuned chat models

🔸 5. Orchestrators
↳ Coordinates multiple agents or tools
⚙️ Tech: Multi-agent systems, cognitive loops, self-reflective LLMs
📊 Metrics: Coordination efficiency, system stability
🛑 Risks: Agent conflict, runaway optimization
🛡️ Fix: Alignment governors, coordination protocols
💡 Examples: Microsoft AutoGen, CrewAI (open framework), LangGraph, OpenDevin

Most teams never get past stage 2.

The easy part is prompts and APIs.

The hard part is this:
→ Memory that doesn't drift
→ Plans that adapt
→ Systems that coordinate

If you can build that, you won't just use prompts and RAG.
You will design behavior.

This is where engineering becomes architecture.

Which stage are your agents in?

♻️ Repost to help more engineers build smarter AI agents
➕ Follow me, Sairam, for practical AI engineering and system design

Evolution of AI Agents

I’ve interviewed 100+ candidates in this brutal AI job market.
This 6-step roadmap is the difference between another rejection and your first offer.

Save this. Share it. Start your prep today.

1️⃣ Coding & Data Structures
↳The first filter in every interview: fluency in code and algorithms.

🔸Python best practices: idioms, typing, testing, profiling
🔸Core DS & Algos: arrays, trees, graphs, recursion, DP
🔸Big-O analysis, trade-offs, optimization

📚Resources:
Effective Python - https://lnkd.in/g9j5AHkx
NeetCode 150 - https://lnkd.in/gwPGw-53

2️⃣ Math & Classical ML
↳Show you understand what powers every ML system.

🔸Linear algebra and calculus basics
🔸Probability, statistics, and core distributions
🔸Regression, k-NN, trees, forests, XGBoost
🔸Bias-variance, regularization, cross-validation

📚Resources:
3Blue1Brown - https://lnkd.in/gn9WR-aE
Andrew Ng ML - https://lnkd.in/gAC6iSWN
Fundamentals of AI - https://lnkd.in/giV_N6KU
Hands-On ML (Geron) - https://lnkd.in/gMMd6Ezu

3️⃣ Deep Learning Foundations
↳Prove you can build, train, and debug models.

🔸PyTorch fluency
🔸Key architectures: CNNs, RNNs, transformers
🔸Training and stabilization tricks
🔸Evaluation & debugging: metrics and guardrails

📚Resources:
DL Specialization - https://lnkd.in/ggYzFu-E
PyTorch Blitz - https://lnkd.in/gN_cUA7h
Understanding Deep Learning (Prince) - https://lnkd.in/g4xgvu_q
AI for the Rest of Us: https://lnkd.in/gTQyc_fi

4️⃣ Specialize in NLP, CV, or RL
↳Go deep in one major area:

🔸NLP: Preprocessing, embeddings, architectures, PEFT, prompting, RAG, evals
🔸Computer Vision: image formation, architectures, generative models, multimodality
🔸Reinforcement Learning: MDPs, policy/value, DQN, PPO, evaluation protocols

📚Resources:
Stanford CS224n - https://lnkd.in/gg-94eKj
Stanford CS231n - https://lnkd.in/gaGWv7ev
Sutton & Barto - https://lnkd.in/gC4HJ3Hd
David Silver's RL Lectures - https://lnkd.in/gxrXm5FM

5️⃣ ML System Design & MLOps
↳Demonstrate you can ship and scale production ML.

🔸ML lifecycle and infrastructure
🔸Serving patterns, scalability, observability
🔸Monitoring, deployment, CI/CD

📚Resources:
Full-Stack Deep Learning - https://lnkd.in/g5XGChfH
Stanford CS329S - https://lnkd.in/gzGhDGgq
AI Engineering - https://lnkd.in/gp-WeZte

6️⃣ Practice & Soft Skills
↳Pair technical excellence with on-the-spot performance.

🔸Timed DS/Algo drills
🔸ML Q&A, system design flow
🔸STAR stories: leadership, conflict, ambiguity, impact

📚Resources:
interviewing. io
Hello Interview

If I were interviewing in 2025, this is the only plan I’d follow.

♻️ Repost to help someone land their dream AI job.
➕ Follow me, Sairam, for proven AI interview and career strategies

Join 24k+ learners from Google, Meta, Netflix, and other top tech companies and level up your AI skills for free at Gradient Ascent: https://lnkd.in/gZbZAeQW

P.S: You need to use only the resources that are relevant to the role you're interviewing for.

Most “AI agents” aren’t agents.
They’re prompts pretending to act.

That’s fine for demos.
It fails the moment you ship to production.

The core mistake: confusing execution with agency.

Prompting can generate text.
It cannot learn from failure, explore alternatives, or improve over time.

If you want systems that adapt, you need Reinforcement Learning.

That’s why Stanford’s new Deep RL course (2025), led by Dr. Chelsea Finn, matters.

It focuses on what real agents actually need:

1️⃣ Learning from experience
→ Model-based & model-free RL
→ Demonstrations, exploration, and offline data

2️⃣ Generalizing across tasks
→ Goal-conditioned RL, meta-RL
→ Unsupervised skill discovery

3️⃣ Operating in the real world
→ Robotics, control, visual navigation
→ High-dimensional states and actions

4️⃣ Turning deep learning into decisions
→ Policies, not just predictions
→ Reward shaping with intent

Bottom line:
Prompting is enough for chat.
Deep RL is required for action.

🎥 Watch the full 19-lecture series here: 🔗 https://lnkd.in/gQ-GirDE

♻️ Repost to push your network beyond the basics. 
➕ Follow me, Sairam, for AI engineering that goes deep.
—-
Ready to bridge the gap between Research and Production? Join 24k+ leaders reading Gradient Ascent to get resources like this first: https://lnkd.in/gZbZAeQW

Need to master the basics first? This has got you covered: https://lnkd.in/gTQyc_fi

taplio

The Battle

Armand Ruiz

Sairam Sundaresan

Followers

Posts

Engagements

Likes

Performance ScoreArmand Ruiz's LinkedIn Performance

Performance ScoreSairam Sundaresan's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Armand Ruiz

Sairam Sundaresan

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Armand Ruiz's LinkedIn Performance

Performance ScoreSairam Sundaresan's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreArmand Ruiz's LinkedIn Performance