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Available for New Opportunities

Kolkata, India

Swarnava DuttaSenior Generative AI Engineer

I build AI products and workflows that help teams search faster, automate routine work, and make better decisions.

Multi-Agent SystemsFine-tuning & LoRARAG ArchitectureMCP Workflows

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build preview
current work
# Example AI workflow
from openai import OpenAI
from langgraph import StateGraph
import pinecone, fastapi, snowflake
classTeamAICopilot:
def__init__(self):
self.focus = [
"Agent workflows",
"RAG + search",
"Model tuning",
"Reliable launches"
]
defbuild_solution(self, needs):
workflow = self.shape_workflow(needs)
return self.launch_for_team(workflow)
# Reliable launches beat flashy demos

Trusted by teams

Work that had to hold up for real teams and real workflows.

Recent delivery has included banking, healthcare, enterprise platforms, and AI-native product environments where reliability mattered as much as speed.

Where it shows up

BankingHealthcareEnterprise platformsRetailRecruitment

The common thread is building systems people can actually use day to day, not just demo once and forget.

MUFG Bank logo
MUFG Bank
Solventum logo
Solventum
Google logo
Google
WhisperIt logo
WhisperIt
3M Health logo
3M Health

Something About Me

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

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

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 0 daily queries while maintaining 0.0% uptime in live environments.

I usually gravitate toward work with a clear result people can feel: less manual effort, faster search, or better decisions. That has led to efficiency gains as high as 0% while keeping the 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.