Head of AI Engineering · AI/ML Executive Leader
I lead AI engineering organizations that turn generative and agentic AI into production platforms, operating workflows, and measurable business outcomes. My work spans multi-model orchestration, platform architecture, and enterprise transformation across mobility, mapping, and AI-native products.
20+
Years Experience
200+
Engineers Led
13K+
B2B Customers Enabled
Up to 70%
Cost Reduction Delivered
Currently Exploring
For Recruiters and Hiring Managers
Available for VP / Head of AI Engineering roles
These systems represent core architectural patterns used in enterprise AI deployments — not just demos, but production-ready implementations.
How I Drive AI Transformation
It requires aligning systems, workflows, and organizations to operate with AI — not just experiment with it.
Designing scalable AI infrastructure — multi-model orchestration, RAG pipelines, vector search, and real-time personalization systems that run at enterprise scale.
Embedding AI into real business processes and decision flows — not as isolated pilots, but as operating capability that changes how work actually gets done.
Aligning engineering, product, and business teams around AI execution — building the operating model, team structure, and governance that makes AI transformation stick.
"The gap between AI experimentation and AI operation is an engineering and organizational problem — not a model problem."
These principles are reflected in how I architect real enterprise AI systems.
Enterprise AI Architecture
From user intent to production execution — connecting AI models, agents, tools, and workflows into real business systems.
How people interact with AI systems
User-facing AI applications for specific workflows
Coordinates tasks, agents, memory, and execution
Selects models, retrieves context, balances cost/latency/quality
Connects AI to business systems and operational data
Measurable enterprise value from AI transformation
"This architecture reflects how I think about enterprise AI: not as isolated models, but as connected systems that combine orchestration, retrieval, tool use, and workflow integration to drive real business outcomes."
These systems represent that architecture in action — production implementations, not prototypes.
Where I Create Value
Six areas where I consistently drive impact — from technical architecture to organizational transformation.
01
Designing multi-model AI platforms that scale from prototype to production — LLM orchestration, RAG pipelines, vector search, and agentic systems built for enterprise reliability.
02
Building the organizational structures, governance frameworks, and team capabilities that allow companies to run AI as a core business function — not as an isolated experiment.
03
Architecting autonomous, tool-using agent systems that execute real-world workflows — from domain-specific agents to multi-agent orchestration with human-in-the-loop controls.
04
Delivering 50–70% cost reductions through cloud-native architecture, Kubernetes-based microservices, and scalable API platforms that handle millions of daily requests.
05
Leading distributed engineering organizations of 200+ across US, Europe, and India — from hiring and culture to delivery execution and cross-functional stakeholder management.
06
Shipping AI products that users and enterprises actually adopt — from India's first agentic AI platform to B2B mapping APIs serving 13,000+ enterprise customers.
Building these systems draws on six areas where I consistently create value.
20+ years building AI platforms, leading global engineering teams, and driving transformative technology strategies.
Three of those roles became defining transformations — here's how they actually happened.
Selected Leadership Impact
Three flagship transformations — the challenge, the decisions, and the outcome.
Head of AI Engineering
March 2025 – Present
Challenge
Fragmented AI capabilities with no unified platform for real-time, production-grade agentic workflows at scale.
What I Led
Key Decisions
Impact
Senior Director of Engineering
Sept 2023 – Feb 2025
Challenge
Scaling a nascent maps platform into a production-grade B2B product while reducing infrastructure costs by 70% and competing with established global providers.
What I Led
Key Decisions
Impact
Director of Engineering — Highly Automated Driving
2005 – 2023 (18 years)
Challenge
Building production-grade AI/ML infrastructure for safety-critical autonomous driving systems, supporting major OEM partners across North America, Europe, and APAC.
What I Led
Key Decisions
Impact
These experiences shaped how I think about AI — and what I've learned along the way.
Lessons from building and scaling AI systems in production.
The pilots work. The demos impress. Then nothing ships to production. The problem isn't the technology — it's that most organizations treat AI as a series of projects rather than a platform decision.
Most of the conversation around agentic AI is still focused on the model layer. That's the wrong level of abstraction. The more important shift is operational.
When you're running AI at scale, model selection is maybe 20% of the problem. The other 80% is system design — and most of that is tradeoff management.