Omkar Srivastava
Thought 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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As an experienced Engineering Manager (a thought leader and people-centric), I bring a diverse skill set and a proven track record of leading successful software projects. With over 14 years of experience in the industry, I have honed my expertise in various domains, including banking, telecom, healthcare, and compliance.
My technical proficiency spans a wide range of technologies, including Gen-AI, Java, Azure, CI/CD, Automation, Python, Java, C#, Robot Framework, Static Analysis, Design Patterns, Platform Engineering & more. I have a strong background in development, having worked on projects using Java, C#, and Python.
I am passionate about empowering my team and fostering a collaborative work environment. My leadership skills have been honed through managing teams across different geographies and time zones. I am always eager to learn new technologies and share my knowledge with others.
As a proud alumnus of IIM Indore with a 3.59/4.33 CGPA, I bring a strong analytical mindset and a strategic approach to problem-solving.
Throughout my career, I have demonstrated a keen interest in growth strategies. I have also contributed to the M365 Core-SCIM Data Retention team at Microsoft, showcasing my ability to work on complex projects and deliver results.
Delivered 50+ sessions successfully in last 2 years. Member of Azure Developer Community and nominated for Microsoft's MVP title. Featured on Times Square New York.
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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