Omkar Srivastava
Top 1% Engg AI Leader| Ex-Microsoft | LinkedIn Top Voice- Al, Leadership, System Design | IIM-I | Lean 6 Sigma, SLII & SAFe Agile Certified | Featured@ Times Square | Mentor | 50+ Sessions
Bengaluru, India
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I've spent 15+ years making sure large-scale engineering systems don't fall apart — and when they do, they recover fast.
From Tier 3 college-> Microsoft -> IIM-Indore Alumni -> Levi's (Strong culture + 175 years old + AI First company). I've led Product development, platform engineering, SRE, data, and AI teams across some of the most complex enterprise environments in the world.
A few things I'm proud of:
🏗️ Built and scaled systems operating at 2B+ signals/day with 99.99% reliability
📊 Delivered $370M+ business impact through BI platforms and data-driven engineering
⚙️ Led 10,000+ releases/year across 100+ products at Autodesk
🤖 Driving agentic AI and LLM automation for next-gen platform and SRE operations
What drives me is a simple belief:
👉 The best engineering teams don't just solve today's problems — they build systems that make tomorrow's problems smaller.
My work spans:
Platform Engineering • SRE • Data Engineering (Kafka, Spark, Snowflake) • BI & Data Modelling • Agentic AI & LLM Automation • Cloud Architecture • MLOps • DevOps (K8s, Terraform, Ansible, Docker)
I lead with a people-first philosophy — empowering teams across geographies and time zones, converting ambiguous problems into scalable solutions, and building cultures where engineers grow as fast as the systems they build.
On LinkedIn I share:
Real lessons from leading platform, data, and SRE teams at enterprise scale
Practical AI + engineering — not theory, actual implementation
Honest takes on engineering leadership, team building, and career growth in the AI era
IIM Indore alumnus | Java • C# • Python | Power BI • Hadoop • Kafka
📍 India | Open to conversations around AI-first engineering, platform scale, and leadership.
Area of Expertise
Topics
Engineering AI Strategy: Scaling Production-Grade Agents with SAP BTP and Microsoft Azure
A "mission-critical" AI system in an enterprise environment requires more than just an LLM; it requires a platform that guarantees security, compliance, and multi-model orchestration.
In this session, I will explore how to architect resilient AI systems by combining the engineering rigor of Microsoft Azure with the enterprise-ready capabilities of SAP Business Technology Platform (BTP).
We will dive into the Generative AI Hub within SAP AI Core to demonstrate how to build "clean core" AI extensions that integrate seamlessly with ERP data without compromising system stability.
Discussion Points:
The Clean Core AI Pattern: Using SAP BTP to build side-by-side AI extensions that keep the digital core stable.
Enterprise RAG: Utilizing SAP HANA Cloud Vector Engine to give LLMs real-time context from business data.
Unified AI Orchestration: Managing multiple models (GPT-4 via Azure, Claude, etc.) through the Generative AI Hub to avoid vendor lock-in.
Operational Governance: Applying SAP AI Core for model versioning and transparent resource logging.
Engineering AI Strategy: Beyond the Hype
Moving from simple LLM integration to building robust AI systems with clarity on infrastructure readiness and cost allocation.
While the industry is saturated with the "what" of Generative AI, engineering teams are still struggling with the "how." Moving from a successful prototype to a production-grade AI system requires more than a fine-tuned model; it requires a fundamental shift in software architecture and operational strategy.
In this session, I will draw from my experience of building large-scale systems at Microsoft and other top product companies to demystify the transition from AI experimentation to engineering rigor. We will move past "prompt magic" to explore the 2026 AI Engineering Roadmap, focusing on the infrastructure, observability, and serviceability required to sustain AI at scale.
Product Development and Management
Covering enterprise product development and Management principles.
GLA University Mathura
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