AI Architect
Designing GenAI systems for enterprises that have to keep running on Monday.
- Solution and architectural design for LLM platforms
- RAG, GraphRAG, agent orchestration and middleware
- AWS, GCP and Azure — production, not proofs of concept
Eight years across Amazon, Cisco, TVS Motor and CBRE — now designing LLM platforms as an AI Architect, and training the engineers who have to run them.
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Years in data & AI
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Enterprises delivered for
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Clouds in production
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Followers on LinkedIn
What I do
Architect and teacher, in the same career. The systems teach me what to train people on; the training shows me where the systems break.
Designing GenAI systems for enterprises that have to keep running on Monday.
Founder and lead trainer at House of Data. 75% practical, 25% theory, by design.
Eight years across analytics, machine learning and NLP at Amazon, Cisco and TVS Motor.
Lead GenAI Engineer roles where the job was delivery, not just modelling.
Track record
Amazon to Cisco to TVS Motor to CBRE to Holland America Group — analytics, then machine learning, then generative AI architecture.
L&T Technology Services · client: Holland America Group · Hyderabad / Pune · Remote
Architecting generative AI capability for a global travel and hospitality group — solution design, platform architecture and the guardrails that let LLM features go to production.
TechStar Group · client: CBRE Asia Pacific · Hyderabad · On-site
Led generative AI engineering for the Asia Pacific arm of a global real estate services firm — owning delivery of GenAI features from architecture through deployment.
TVS Motor Company · Bangalore · Full-time
Senior data science across manufacturing and commercial problems at one of India’s largest two-wheeler manufacturers.
Founder
Founder, CEO & Lead Trainer · Dilsukhnagar, Hyderabad
An AI edtech company built around one belief: almost nobody teaches production-grade AI. Courses stop at the notebook and skip RCA, real bug fixing, sprint planning and the software lifecycle. House of Data does not.
Visit houseofdata.inWriting
Longer pieces on what actually happens when generative AI meets production.
Most retrieval failures get blamed on the model. In production they almost always trace back to how the documents were split — and that is a much cheaper thing to fix.
LangChain, LangGraph, AutoGen and the rest get you to a working demo fast. What they do not give you is an operable system — and the gap between those two things is where most agent projects stall.
Running live cohorts changed how I architect. When you watch people try to operate a system you designed, you find out very quickly which of your decisions were actually good.
AI Radar
Model releases, framework updates and the news worth reading — collected automatically, four times a day.
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Toolkit
LLM & Agents
Cloud & Platform
Document AI
ML & Data
Engineering
That is the easy part. Bring the architecture and we will find what breaks first.