Sharad Bajaj — VP Engineering, Microsoft | Agentic AI & Data Platforms | Building Systems that Make Decisions, Not Predictions | Ex-AWS | Author
VP Engineering, Microsoft | Agentic AI & Data Platforms | Building Systems that Make Decisions, Not Predictions | Ex-AWS | Author
Sharad Bajaj ranks #739 of 19,530 LinkedIn creators in Computer Software, and is a standout voice in United States. They have 28.5K followers and published 12 posts in the last 30 days at a 0.3% average engagement rate.
- 28.5K followers
- 12 posts / 30d
- 0.3% avg engagement
- 385 follower growth / 30d
The roast
Sharad claims he builds AI systems that make decisions rather than predictions, which explains why his own profile’s algorithm decided 28,000 people didn't need to see a single one of his posts this month. He is the human embodiment of a RAG pipeline: he pulls information from everyone else’s success and fails to ground it in a single personality.
About Sharad
I am a senior engineering executive with 25+ years of experience building and scaling enterprise software, cloud platforms, and AI-native systems used by millions of customers globally.I was a founding member of Microsoft Teams and have since led global engineering organizations across Microsoft and AWS Amazon Connect (CcaaS), spanning large-scale distributed systems, data platforms, and generative AI.Today, my focus is on building AI-native enterprise platforms where LLMs, data systems, and agent workflows come together to deliver real business outcomes.My work includes:• Agentic AI systems and AI agents that can reason, act, and integrate with enterprise workflows• LLM platform architecture, including Retrieval-Augmented Generation (RAG) and grounding systems• Evaluation-driven development (LLM evals) to improve reliability, safety, and quality over time• Data + AI platforms that unify analytics, applications, and decision systems• Responsible and trustworthy AI, including governance, compliance, and explainabilityI believe enterprise AI fails when treated as a model problem.It succeeds when designed as a system grounded in business semantics, constrained by real-world data, and measured by decision quality, not just model accuracy.I focus on operationalizing this through:• Business ontology and semantic layers for enterprise AI• Decision-grade platforms connecting data, models, and actions• Scalable AI infrastructure designed for production, not demos• Alignment across engineering, product, and business teamsAcross my career, I’ve learned the hardest problems are not model performance, but:• Designing for real-world failure modes• Scaling systems under production constraints• Building AI systems that earn long-term trustI am particularly interested in connecting with leaders working on enterprise AI platforms, agent ecosystems, LLM infrastructure, and evaluation-driven systems.
Highlights
- Top 5% Audience — 28,530 followers
- Top 5% in Computer Software — Ranked #155 of 4763 creators
- Top 5% in United States — Ranked #293 of 6030 creators
- Top 10% Creator — 13 posts in 30 days
Recent posts
The "Broken Window Fallacy" of the AI Era I was recently reading Henry Hazlitt’s classic Economics in One Lesson and I couldn’t help but notice a striking correlation with how we view the current wave of Agentic AI. Hazlitt famously dismantled a major economic blind spot known as the Broken Window Fallacy. He argued that bad economic thinking focuses only on the immediate, visible effects of a shift on one specific group, while completely ignoring the long term, invisible consequences for everyone else. He called it the difference between The Seen and The Unseen. We are seeing this exact f
13 reactions · 2 comments · 1 reposts
The "1-to-10" Rule: Why the future belongs to the Producers. "I am replaceable." It’s a grounding reality for anyone in leadership or engineering. No matter how much value we think we add, systems evolve, organizations scale, and roles change. But there is a massive shift happening right now in how we define that replacement value. It used to be about headcount: "If I leave, it takes 3 or 4 people to cover what I do." Today, that math is completely broken. The future isn't about how many bodies it takes to replace you. It’s about the force multiplier you build around yourself. The most v
125 reactions · 23 comments · 4 reposts
EPPC26 is a wrap, and what a week it was. Denmark was amazing, but what stood out even more was the energy around AI. The excitement from customers, partners, and the community was palpable. AI is here. The conversation has shifted from "what if" to "what's next." I enjoyed sharing how Dataverse is helping power this transformation by providing the business intelligence layer that enables both business agents and coding agents to operate with context, knowledge, and trust. Thank you to everyone who attended our sessions and shared their perspectives. I left inspired by what customers are al
125 reactions · 3 comments · 2 reposts
Last updated 2026-08-01