Anil KumarAI Engineer
Generative AI • Machine Learning • RAG • Agentic AI
I design and ship production AI systems — RAG pipelines, autonomous agents and machine learning services — and integrate them into the enterprise platforms, APIs and cloud infrastructure businesses already run on.
Key capabilities
What I build
Four areas where I deliver end to end — from retrieval design to a monitored service running in production.
01
Retrieval-Augmented Generation
Chunking strategy, embeddings, vector search, reranking and grounded answer synthesis with citations.
02
Agentic AI
Planning, tool calling, multi-step workflows, validators and human-in-the-loop control points.
03
Machine Learning
Classification, forecasting, anomaly detection and recommendations, from feature pipeline to served API.
04
AI Platform Engineering
FastAPI services, streaming responses, caching, observability, evaluation harnesses and guardrails.
Featured projects
Systems, not demos
Each project below has a documented architecture, evaluation approach and set of engineering decisions.
AI architecture
How a production RAG pipeline fits together
Hover any component to see the role it plays. Full agentic and machine learning architectures are on the architecture page.
Hover or tap a component to see what it does in this architecture.
Technology highlights
The stack I work in
Modern AI engineering combined with the enterprise platforms and cloud services businesses already depend on.
AI & Generative AI
Machine Learning
Data & Engineering
AI Development
Cloud
Enterprise Technology
Experience
AI engineering on an enterprise foundation
Nine years across enterprise platforms, machine learning and generative AI — which is why the AI I build survives contact with real systems.
AI / Generative AI Engineer · Enterprise Technology Consultancy
2023 — PresentDesign and deliver production generative AI systems: RAG pipelines, agent workflows and governed AI APIs integrated with enterprise platforms.
Machine Learning Engineer · Data & Analytics Practice
2021 — 2023Built and served predictive models across churn, anomaly detection and recommendation use cases with reproducible training pipelines.
Enterprise Platform Engineer · Financial Services Sector
2017 — 2021Delivered enterprise CRM, marketing automation and API integration work across regulated banking and customer engagement platforms.
Professional impact
How I work
Principles that show up in every system I ship.
Grounded by default
Every retrieval system I ship answers with citations and abstains when evidence is missing.
Evaluated before rollout
Golden datasets and automated eval harnesses gate releases instead of subjective spot checks.
Safe automation
Agents operate under step budgets, typed tool schemas and validation gates before any write action.
Enterprise-ready delivery
AI services ship with auth, quotas, redaction, monitoring and cost attribution from day one.
Let's Build Intelligent Solutions
Hiring for an AI engineering role, or scoping an AI system? Tell me the problem and I'll tell you honestly whether AI is the right tool for it.
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