summary

A Profit-driven AI/Ml/GenAI Leader offering nearly 25 years of experience in technology strategy, large-scale AI adoption, and digital transformation across BFSI, telecom, mining, retail, education, and manufacturing. Proven track record in defining enterprise AI Strategies, multi-year roadmaps, and operating models that align AI initiatives to business priorities and regulatory requirements.
Specialized in AI strategy & governance — including AI readiness assessments, use-case discovery and prioritization, KPI/ROI frameworks, and governance models covering data strategy, model risk, privacy, and responsible AI principles. Experienced in guiding cultural and operational shifts for AI adoption, from stakeholder alignment to new ways of working.
Hands-on in AI implementation and architecture: designed reference architectures for GenAI/RAG platforms, document intelligence, AIOps, and predictive analytics; built scalable data pipelines; and guided cloud-native deployments and MLOps practices on AWS, GCP, and Azure in complex, regulated environments.

Key Highlights:

Defined and executed a 3-year enterprise AI/GenAI roadmap for a $100M+ technology-sector client, including governance, operating model, and platform recommendations adopted by senior leadership.
Delivered 4+ GenAI use cases into production and led 20+ AI/GenAI POCs across industries (document AI, RAG for knowledge, multilingual chatbots, AIOps, predictive maintenance, fraud/safety).
Built AI/ML/GenAI & Data Engineering offerings and added 3 new AI-focused accounts with $10M revenue and potential to double in two years.
Led cross-functional teams of 200+ across geographies; recognized as “Inspiring Leader in AI & Analytics” (3AI Zenith Awards).

SKILLS

· AI/GenAI adoption strategy and multi-year roadmaps

· AI readiness assessments (data, infra, skills, operating model)

· Use-case discovery, value/feasibility assessment, and prioritization

· KPI/ROI framework design linking model metrics to business outcomes (cost reduction, risk mitigation, revenue uplift, time-to-insight)

· AI governance models: data strategy, model risk management, interpretability, audibility, documentation standards

· Responsible and ethical AI (fairness, bias, privacy, regulatory alignment in BFSI/public sector)

Stakeholder facilitation and C-suite storytelling; translating complex AI topics into clear business narratives

AI platforms & reference architectures (RAG, document AI, AIOps, conversational AI)

· Data pipelines & integration: ETL/ELT, streaming, event-driven integration, APIs

· MLOps: CI/CD for ML, experiment tracking, model registry, monitoring & observability, human-in-the-loop workflows

· Scalable deployment patterns: microservices, containerization, orchestration, and API-based integration with enterprise systems