Udayan S.

📍 Product Role Transition Track Stories She had: ✅ IIT Bombay Research background ✅ Deep AI/ML expertise ✅ Patent filings ✅ Award-winning AI innovations ✅ Multiple AI initiatives incubated into business opportunities Yet the market kept calling her for: 👉 Data Scientist roles 👉 AI Research roles 👉 Innovation Specialist roles Not the Product roles she was already operating in. That was the challenge. Not capability. Interpretation. When she came to me, she wasn't asking: "How do I become a Product Manager?" Her real question was: "Why does the market keep seeing me as a Data Scientist when most of my work already looks like Product Leadership?" And honestly... It was a fair question. Because after reviewing her profile, one thing became obvious. The market was reading: ✔️ AI. ✔️ Research. ✔️ Innovation. ✔️ Technology. But missing: ❌️ Product ownership. ❌️ Customer discovery. ❌️ Roadmap influence. ❌️ Business decisions. ❌️ Strategic prioritization. Exactly the signals hiring panels use for Senior PM and AI Product Leader roles. In our first session, the diagnosis became clear. The challenge wasn't transitioning into Product Management. The challenge was helping hiring managers recognize the Product Management work she was already doing. Over the Product Role Transition Track, we worked on: ➡️ Reframing AI innovation into product outcomes ➡️ Translating discovery work into PM language ➡️ Surfacing roadmap influence ➡️ Highlighting business impact ➡️ Positioning her as an AI Product Leader rather than an AI researcher One shift changed everything. From: "I built AI solutions." To: "I identified opportunities, validated customer problems, prioritized investments, aligned stakeholders, and converted emerging technologies into business outcomes." Same experience. Different interpretation. 🎯 A note she shared after our session: "With one session she was able to validate my fitment, explain the gaps in my CV and give a fairly good idea on what should be possible next steps." "I realized she had been very diligent to go through my profile and understand all of it and put together a good amount of insights in less than a day." The biggest misconception in AI hiring today: Many AI leaders are already performing Product Management work. The market simply doesn't classify them that way. And hiring decisions happen on interpretation. Not intention. 💯 2026 AI PRODUCT HIRING REALITY The market rarely rejects capability. It rejects unclear positioning. If you're coming from: 👉 AI Research 👉 Data Science 👉 Innovation 👉 Applied ML 👉 IIT / Academic backgrounds Your challenge may not be becoming a Product Leader. Your challenge may be proving you're already one. 👇 COMMENT AI What's the market misunderstanding about your profile? #AIProductManagement #ProductLeadership #ArtificialIntelligence #ProductManagement #DataScience #IITBombay #AIInnovation #CareerGrowth #ProductStrategy #Leadership #ProductManager

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🦾🧠 Building Deep Research Agents ?? Adding think tags can collapse training making model stuck in long reasoning trajectories Authors revamped search-R1 strategy for deep research. They decoupled the entire deep research dimensions along with enhancements as following: > prompt template: proves fast thinking templates are more stable and generates short response length. Slow thinking templates get stuck in <think> tags > modified F-1 penalizes the reward to discourage omitting search or answer steps. This makes the policy to avoid being collapsed into a simpler state of generating no answer > policy optimization: authors found GRPO gets noisier in multi-step long context reasoning. PPO gets challenging as it tries to fit value function over sparse outcome rewards (Exact Match based). Found REINFORCE stays table with lowest search frequency Papers shows high promises while introducing simpler updates to search-R1. Overall I am intrigued by the findings that slow thinking could: > de-stabilise the training > longer reasoning chains > multiple search queries > often leading to answer avoidance

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Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

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