AI Engineering Leader | Built Agentic AI at Krutrim & Ola | LLM Platforms · AI FinOps · 200+ Teams | Chicago
I've spent the last 20 years building and scaling technology platforms-from cloud transformation to what we're now seeing with Agentic AI. What I care about is simple: turning AI from something that looks impressive in a demo into something that actually delivers business value at scale. At Krutrim, I led teams building India's first agentic AI platform (Kruti.ai), working across multi-model orchestration, real-time personalization, and production-grade systems. At Ola, I helped scale mapping and location platforms to support 13,000+ B2B customers. Across both, the focus has been consistent-take complex systems and make them reliable, efficient, and commercially viable. A big part of my work sits in the gap most companies struggle with moving from experimentation to production. That means designing multi-agent workflows that go beyond chat, driving 40-70% cost reductions through smarter model strategies, and building the governance layer that lets enterprises actually trust what they're deploying. I've also spent a significant part of my career building and leading global engineering teams across North America, Europe, and APAC-creating environments where teams can move fast, challenge ideas, and still stay aligned to business outcomes. Right now, I'm focused on helping organizations move past the "PoC stage" and actually operationalize AI-especially in environments where scale, cost, and trust matter. I'm based in the Chicago area and always open to conversations around AI strategy, platform engineering, and where this space is heading next.
Most AI programs fail in production because cost discipline, governance, and operational ownership are bolted on too late.
Signature System: AI Evaluation Showcase
Offline eval suites, live drift monitoring, hallucination indicators, and regression-minded quality gating are built into this platform.
Why this matters: quality regressions are surfaced before release, so AI reliability is managed as an engineering system.
Explore Signature SystemYears Experience
Engineers Led
B2B Customers Enabled
Cost Reduction Delivered
Currently Exploring
For Recruiters and Hiring Managers
Available for VP / Head of AI Engineering roles
I build and scale production AI platforms with evaluation loops, governance controls, and measurable business outcomes.
Signature review artifact: AI Evaluation Showcase (offline + online quality loop).
Trust & Governance at a Glance
Guardrails: Centralized prompt-injection and output safety checks across AI routes.
Human Oversight: Approval checkpoints on high-impact transitions before strategist output is released.
Quality Loop: Offline eval suites plus online drift monitoring and hallucination indicators.
Abuse Protection: Upstash-backed rate limiting with privacy-preserving IP hashing.
Auditability: Decision traces and trace IDs are visible for end-to-end review.
Responsible disclosure policy: security.txt
These systems represent core architectural patterns used in enterprise AI deployments — not just demos, but production-ready implementations.
How AI Quality Is Measured
Offline LLM-as-Judge eval cases with semantic fidelity scoring.
Online drift snapshots with hallucination and anomaly indicators.
Regression-aware quality gates designed for release readiness.
Local-First AI Demos
RAG, Vector Search, Multimodal, and Quantization run in-browser with client-side inference paths.
This reduces server-side data exposure for demo workloads and showcases privacy-aware execution patterns.
Trade-off is explicit: local execution improves privacy/cost posture, while server models handle heavier reasoning workloads.
Closed-loop LLM evaluation pipeline — semantic fidelity, hallucination detection, guardrails, and CI gating in action. Demonstrates the quality loop recruiters and CTOs look for: offline eval coverage, online drift monitoring, hallucination indicators, and CI-ready regression gating.
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. This includes ensuring AI augments human judgment rather than replacing oversight in critical decisions.
"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
Five high-signal 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.
Building these systems draws on five high-signal 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
Build a production-scale Agentic AI platform for mobility, commerce, and payments while unifying fragmented models, vendors, and workflows into one reliable operating system.
What I Led
Key Decisions
Impact
Senior Director of Engineering
Sept 2023 – Feb 2025
Challenge
Scale Ola Maps into a core cloud-native mobility platform serving enterprise customers at high reliability while materially reducing infrastructure spend.
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.
Open to VP / Head of AI Engineering roles. I focus on organizations building or scaling enterprise AI — where platform architecture, agentic systems, and transformation execution matter. I respond to every serious inquiry within 24 hours.
Portfolio
https://www.prasadkavuri.com