---
title: "Debanshu Mutt - Software Engineer in Bengaluru | Tal"
description: "Meet Debanshu Mutt, SDE-2 | Backend Engineer | Python | LLMs | Agentic AI | AI Orchestration | Distributed Systems | Ex-H&M / Maersk based in Bengaluru. AI stack includes claude code, cursor, and hugging face."
image: "https://tal.club/meet/debanshu-mutt/share-image"
---

# Debanshu Mutt on Tal

## Headline

SDE\-2 \| Backend Engineer \| Python \| LLMs \| Agentic AI \| AI Orchestration \| Distributed Systems \| Ex\-H&M / Maersk

## About

Backend Engineer with 4\.5\+ years of experience building scalable, cloud\-native distributed systems across retail and logistics\. I specialize in Python/ FastAPI/ Flask, designing event\-driven microservices and data platforms that handle millions of requests daily\. Recently, I’ve worked extensively on production\-grade Generative AI systems, building LLM\-powered workflows using LangChain, FAISS, Redis, and Azure/GCP\. My experience includes RAG pipelines, secure MCP\-based data access, and scalable AI\-driven APIs handling 50k\+ daily requests\. Strong in distributed systems, Kubernetes, messaging platforms \(Kafka, RabbitMQ\), I focus on building resilient, observable, and high\-impact backend and AI platforms\.

## Location

Bengaluru

## AI stack

claude code

cursor

hugging face

langchain

langgraph

mcp

## Work experience

Software Engineer at Publicis\.Sapient, Jun 2026 \- Present · 4 mo, Bengaluru, Contributed to the AI\-powered AppMod platform using Python, FastAPI, Agno agents, LLMs, and LanceDB, engineering code modernization workflows that reduced costs by up to 50%\. • Enhanced the AI chat module with intent classification, tool scoping, and lexical ranking to limit retrieval to the top 30 capabilities, reducing latency by 30% and LLM retry failures by 65%\. • Integrated Redis\-backed WebSocket connection management with polling fallback, reducing redundant frontend polling by 80% and enabling real\-time updates for large\-repository async workflows\. • Engineered cooperative cancellation across code ingestion, spec, and Epic/Story workflows, reducing Cancel API latency to under 500ms and avoiding 40% of downstream LLM calls while preserving partial artifacts\. • Built per\-step LLM token telemetry for the code ingestion pipeline \-tracking input, output, cached, and failed tokens\- identifying optimizations that reduced LLM token consumption by 15%\.

Software Engineer \(SDE\-2\) at H&M, Jul 2024 \- Apr 2026 · 1 y 9 mo, Bangalore Urban, Built a scalable event\-driven system with FastAPI handling 3M\+ requests daily, syncing PLM data in real time via RabbitMQ, Spanner, Kafka and Solace\. • Refactored over 3,000 lines of legacy code using SOLID principles, reducing bug resolution time by 75%\. • Designed and owned a Python\-based data ingestion pipeline \(Postgres \- BigQuery\) deployed on Kubernetes with dynamic configs, improving data availability for 5\+ teams and reducing manual effort by 60%\. • Built a GCP\-powered logging pipeline from Cloud Run to Log Sink and Pub/Sub, powering Splunk dashboards; improved monitoring coverage by 55% and reduced incident response time by 40%\. • Built a secure LangGraph\-powered FastMCP backend that orchestrated intent classification, tool selection, and SQL generation for PLM analytics, enforcing OAuth\-based supplier isolation across 5\+ teams and reducing ad\-hoc requests by 70%\. • Developed shared Python utilities and mentored juniors via PR reviews, reducing duplication and speeding up feature delivery by 25%\.

Associate Full Stack Engineer at A\.P\. Moller \- Maersk, May 2022 \- May 2024 · 2 y, Bengaluru, Developed a scalable backend service for the GenAI Agent Assist project using Python, FastAPI, LangChain, and Azure Functions\. Orchestrated intent classification, RAG, and MCP tool workflows to generate LLM\- powered recommendations, processing 50K daily requests and saving $4M annually\. • Engineered a robust REST API solution for the Pricing Team using FastAPI and MySQL\. Significantly reduced data retrieval time by 70% through optimized database queries and the implementation of data partitioning logic\. • Designed a Flask API using OCR and OpenCV to automate release order document authorization, leveraging multiprocessing and PyMuPDF to reduce response time from 40 to 5 seconds\. • Implemented CI/CD pipelines using GitHub Actions, streamlining development and deployment\. Deployed APIs in Docker containers, leveraging containerization for efficient scaling and maintenance\. • Utilized Microsoft Azure Cloud \(Web Apps, Function Apps, ACR\) services and rule\-based scalability to host and manage APIs, ensuring efficient handling of fluctuating loads while optimizing costs\.

Full Stack Engineer Intern at A\.P\. Moller \- Maersk, Jun 2021 \- May 2022 · 1 yr, • Automated diverse business use cases utilizing Java, Apache POI, Sikuli, and Selenium, leading to annual savings of around $5M for the company\. • Utilized PowerBI and DAX measures to create visuals, providing pricing managers insights into week\-to\-week volume pressure and pricing patterns\. Maintained data pipelines using SQL, Databricks, etc\., resulting in a 30% reduction in manual effort\.

## Education

Bachelor of Technology \- BTech, Computer Science and Communications, KIIT \- Kalinga Institute of Industrial Technology

High School Diploma, DBMS English School

[View the original profile](https://tal.club/meet/debanshu-mutt)

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