Kunal Khadilkar

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝗺𝗮𝗿𝗸𝗲𝘁 𝗯𝗲𝗲𝗻 𝘂𝗽 𝘁𝗼 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟮 𝘄𝗲𝗲𝗸𝘀 𝗼𝗳 𝟮𝟬𝟮𝟲? Here are the emerging trends in the data and AI talent landscape based on my meetings with Heads of AI and CDOs along with a note on the churn at C-level ✅ High demand for hands on data and AI engineers which is also driving higher salary packages ✅ High demand for data scientists that have put GenAI products and solutions into production - this is fueled to a certain extent by AI CoEs ✅ High demand for AI governance specialists ↘️ Low demand for data scientists that have limited experience with putting AI solutions into production ↘️ Low demand for hands off managers ↘️ Low demand for pure data analysts. While the head of AI/ CDO function is still in its infancy in many organizations. Which means, data/ AI maturity is low especially from a culture perspective. However, expectations on what these teams need to deliver are sky high because of the narrative built externally based on how companies want to be perceived by their shareholders/ customers. As a result, the pressure on these teams and their leaders is immense. This often leads to a revolving door situation for CDO/ head of AI roles. The prediction I made late last year regarding change of guard at the CDO level is already playing out in many banks and FS companies. ♻️ If you found the insights valuable, please share it so that people from your network can benefit 📍 Except for the image, no AI was used to create this post!

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95% of the companies will ask you this in one of the APM hiring rounds. These two rounds sums up the entire APM hiring process. 1. Case Study Round - Most of the applications gets filtered out in this round only. Approaching a problem in depth is very important in a case study,  with lots and lots of things to add in the case study, PM aspirants often get confused in this. The below link contains, shortlisted product decks, which might help you in giving a structure to your approach. https://lnkd.in/g2SiUvbm 2. Interview Rounds In most of the PM interviews generally five types of questions are asked - 📌 Root Cause Analysis 📌 Product Design 📌 Guesstimates 📌 Metric Analysis 📌 Favorite Product Most of the PM aspirants are not able to solve these cases because they do not have a structural approach to a problem. To solve this I am sharing the structured approach that helped me successfully clear interviews. You can download from the link below - 1. Master Product Case study (15+ solved interview questions asked by leading companies) - https://lnkd.in/gJbb5bj7 2. For Metric Analysis and Favorite Product  https://lnkd.in/giZzySEF 3. For RCA, Product Design and Guesstimates https://lnkd.in/g5BQi7zN #100daysofproduct #100daysofproductchallenge #productmanagement

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Today, I tuned into another session of The Product Folks #Mosaic AI Buildathon, which featured Madhu Gurumurthy (Head of Product, Gemini @ Google). He discussed how product teams are evolving and what skillsets product leaders need to adopt moving forward. Madhu started by outlining what great teams often share: 1. Technical PMs with strong design sense (they know what good design looks like) 2. Engineering leads with good product and design sense 3. Designers with product sense and understanding of technical possibilities 4. Product leaders who hold enough context to make quick decisions Having all four on one team is rare, but when it happens, the team operates as one unit. The more cross-functional awareness each role has, the better the team performs. The reality, he said, is that most teams still operate with hard-ish handoffs and try to compensate for this with standups, syncs, alignment meetings, documentation, and layers of leaders - an approach that is bloated, inefficient and slow. He believes AI will reshape this dynamic, giving rise to a new “builder” archetype - people with judgment across product, design, and engineering who can leverage AI to prototype and iterate quickly. Instead of a PRD-heavy process taking 4–6 weeks, builders can create a prototype in a single day. The team can then debate it, have the builder make refinements, and hand it off to engineering to begin the build phase. He pointed out that Spotify has already introduced prototyping into their PM interviews. Madhu sees the future as one where big companies run smaller, flatter teams shipping more products, and startups thrive with small teams and solo founders. Engineering will continue to be super important. Those with strong product/design sense will have an advantage because they can own things end-to-end. His advice: • Start building in your own time. • Manage your information diet (learn from the frontier, not from hype.) • Read papers and grasp the salient points to sharpen your intuition. • Study good AI products. Focus on what they do well, what prior constructs for that category they are challenging, and what product patterns they are inventing. • Learn to build evals. Madhu emphasized that what will remain constant is the hard work required to build high-quality, successful products (ie. having a vision, identifying what user and market problems it is solving for, and how.) Thanks to Madhu and the TPF team for another great session. Really enjoying the insightful conversations around these topics. #CSstudent #ProductManagement #AI #ProductBuilder #CareerAdvice #MosaicByTPF

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Ruokai Yin, our summer intern from Yale to Microsoft Azure, worked with Xuan Zuo, @Sattwik Deb Mishra, Hokchhay T. and Preyas Shah and me. We published a paper based on his project: https://lnkd.in/eHuG3BSt Summary of paper: Finding optimal sharding is hard especially for large LLMs. A large number of devices have to be divided into TP, PP, EP dimensions. For TP, we have to decide the tensor dimension e.g. head, hidden size to shard for every op. Additionally, TP can be one-dimensional/two-dimensional etc. This makes for a large sharding space that is hard to cover exhaustively. SoTA methods use heuristics to select sharding e.g. GSPMD, Sequence Parallelism, Helix etc. There are significant time gaps, months or years, between the discovery of each of these methods. We want to fast-forward the process of discovery by 1) using a formal representation of the sharding space, and, 2) using reinforcement learning to search the sharding space.    This paper demonstrates that our method is 3.5x better than random search, and on par with hand-tuned heuristics when looking only at 1D sharding and without allowing sequence parallelism. We aim to extend the work to multi-dimensional sharding as well as sequence parallelism. Beyond that, we wish to combine the problem of sharding with fusion, tiling and collectives as well.

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H1B Visa fees 100,000 Dollar. Time to awake indians. No other way then to get out of delusion of development. We should focus more on economy then cultural activities going on now a days. This visa row is indication of following 1. AI is really on the way to destruct and change IT sector forever. 2. Jobs placements will be lower then expected. 3. Even if this visa fees is reduced then also there will be an uncertainty on the AI changing the world order. 4. Narratives build that now india will be great again, is false i think. 5. Only way to avoid heavy losses is to truly invest in AI research.

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Regulatory uncertainty surrounding the H-1B visa program is creating a significant talent crunch for U.S. companies, particularly for Indian-led AI startups. According to a recent Linkedin News report, the current climate is causing H-1B holders to hesitate leaving stable Big Tech jobs for startups, fearing instability. This is even affecting lateral hires, who are reportedly backing out of offers. In response, startups are adapting their hiring strategies: 🔹 Prioritizing recruitment of U.S. citizens, green card holders, and F-1 student visa holders. 🔹 Shifting to a bifurcated model: hiring go-to-market roles in the U.S. while sourcing tech talent directly from India. A key point of clarification from USCIS on the new $100,000 H-1B fee: ✅ Applies to: Only new H-1B petitions for workers currently outside the U.S. who do not have a valid visa. ❌ Does NOT apply to: Status changes (e.g., F-1 to H-1B), amendments, or extensions for individuals already within the U.S. This distinction may benefit Indian students currently in the U.S. on F-1 visas, as they are exempt from this fee. Overall, these policy shifts are reshaping how U.S. companies approach international hiring and visa sponsorship. #H1B #USTech #Startups #TalentAcquisition #Immigration #HiringTrends #AI #India

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Hey LinkedIn fam! 🚀 Time for a quick dive into what I’ve been building, dreaming, and training (yes, both AI and abs 😅) — all geared toward one vision: reshaping the present while laying the groundwork for the AGI future. 👇 1. 🔧 AirBorneHRS : Currently developing personalized AI agents for HR consultancy that don’t just automate—they understand. We’re talking intelligent virtual employees that adapt, learn, and make decisions. → The bigger play? Scalable agent architectures that could serve as cognitive limbs for AGI systems. 2. 🧠 F.R.I.D.A.Y. AI Assistant: This isn’t just a voice assistant. It writes, debugs, and evolves code like a real junior dev—and I’m training it to keep iterating until the output works. Soon, it won’t just respond to commands. It'll ask, "Should I refactor this based on your pattern?" → We're prototyping tool-using, memory-equipped agents with persistent identity—hallmarks of AGI. 3. 🧬 CBT Engine for Minds & Machines: Creating a cognitive behavioral therapy engine that doesn’t just follow scripts—it tracks emotional and logical intent across sessions. → Why? Because AGI needs more than reasoning; it needs reflective, context-aware adaptability. That starts here. 4. 💪 Meanwhile, in the real world… I’m training for GreatManOfTheUniverse 2025 while doing 24-hour fasts and chasing that classic Hemsworth build. And yes—code reviews happen right after shoulder day. 🤖🔥 What’s next? 🔭 Publishing AGI research in parts: Mirror Mind (Transformer vs. RNN-based agents), economic bots, and eventually, autonomous agents for decision theory. Think chess today, Wall Street tomorrow. We’re closer than we think—and the best part? We’re building it. Would love to connect with fellow builders, researchers, or anyone who thinks GPT-5 is just the beginning, not the peak. #AGI #ArtificialGeneralIntelligence #AI #Startups #MachineLearning #Futurism #FitnessMeetsTech #AIRBorneHRS #CBTEngine #VoiceControlledCoding — Suryaansh Prithvijit Singh

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THE MULTIPLIER EFFECT 🚀 Mentorship isn't just about sharing advice, it’s about scaling impact. I’m honored to share this Letter of Recommendation from nasscom following my work with their Mentoring Program. Over 4 cohorts, I’ve had the privilege of mentoring 10+ professionals, helping them bridge the gap between complex product theory and real-world execution. The focus? Moving beyond the roadmap. In our sessions, we focused on the "invisible" skills that define senior leadership: 🔹 Investment Strategy – Shifting from "what we build" to "how we create value". 🔹 Portfolio Thinking – Managing long-term strategy in a dynamic B2B SaaS ecosystem. 🔹 Structured Decision-Making – Navigating stakeholder management with clarity. A big thank you to Khushbu Gupta, Shreya Sharma, and the nasscom team for the opportunity to give back to the product community. Seeing my mentees drive impact within their organizations is the best reward. #ProductManagement #Mentorship #B2BSaaS #NASSCOM #ProductStrategy #Leadership #IndiaTech #ProductThinking #AI

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