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Get to know me

Lead AI Architect in Kolkata, India, with 6 years of generative AI engineering behind me. This page covers how I think about building AI systems, the principles I work by, and what I've learned shipping them into production.

Professional profile

I build AI systems that teams can still trust after launch.

I'm an enterprise AI architect based in Kolkata, India. For 5+ years I've worked across data engineering, search, and generative AI, building systems where answer quality, reliability, and business value all matter at the same time.

Multi-Agent SystemsFine-tuning & LoRARAG ArchitectureMCP WorkflowsEvaluation Pipelines

What I focus on

As a Lead AI Architect, I care about AI systems that keep working once teams start relying on them. That mindset comes from my years in cloud data platforms, automation, and reliability-focused engineering.

Much of my recent work centers on LangGraph orchestration, RAG, and domain-specific AI products. Recent platforms have supported 10K+ daily queries while maintaining 99.9% uptime in live environments.

I engineer for outcomes teams can measure: lower latency, automated workflows, and faster decisions. That focus has delivered efficiency gains as high as 67% while keeping each system clear enough for teams to maintain after launch.

How I work

The things I care about when building AI systems.

Quality

Measure the behavior before you trust the behavior.

Strong AI systems earn trust through clear results and careful testing. I set up evaluation early so teams can see what is working and improve with confidence.

Retrieval quality is checked before scale becomes the priority.

Answer quality is reviewed with clear task-based checks.

Decisions stay tied to user needs and business goals.

Reliability

Build for everyday use, not only for demos.

The best AI products keep working when data is messy, traffic grows, and more than one team needs to use or support them.

Workflows are kept clear before complexity piles up.

Latency, fallbacks, and caching are planned from the start.

Monitoring and team handoff are considered alongside model behavior.

Usefulness

Make the result useful enough that people keep coming back.

The most valuable AI work saves time, improves decisions, or removes repetitive effort. That is the standard I like to build toward.

The business goal stays visible from discovery through launch.

Security, guardrails, and handoff readiness are built in early.

The final system should feel easy to use, extend, and trust.

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