---
title: "Ayan Mondal - Senior Software Engineer in West Bengal | Tal"
description: "Meet Ayan Mondal, Senior Backend + GenAI Engineer | FastAPI · RAG · Agentic AI · LangGraph | Qualcomm · Experian | Production APIs serving 2M+ users based in West Bengal. AI stack includes codex and claudecode."
image: "https://tal.club/meet/ayan-mondal-1/share-image"
---

# Ayan Mondal on Tal

## Headline

Senior Backend \+ GenAI Engineer \| FastAPI · RAG · Agentic AI · LangGraph \| Qualcomm · Experian \| Production APIs serving 2M\+ users

## About

Most engineers are either solid on backend or excited about AI\. I do both and I've learned that the line between them barely exists when you're building systems that need to stay up\. Over 5 years across Qualcomm and Experian, I've architected production systems that move real numbers: a RAG chatbot serving 2M\+ users with 92% satisfaction, an agentic log\-analysis platform that cut debug turnaround by 60% for 300\+ engineers globally, an ASR pipeline that halved transcription costs \($80K → $40K/yr\), and a GenAI email assistant that tripled SDR reply rates across 500\+ B2B sales teams\. The backend work is what keeps the AI work standing\. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → FastAPI microservices handling 1,000\+ concurrent requests with async I/O, JWT auth, and structured error handling → Cloud infrastructure on AWS: SageMaker \(20\+ endpoints\), Lambda, S3, with MLflow monitoring and 35% latency reduction → Databases across the stack: PostgreSQL, MongoDB, Redis, CosmosDB — chosen for the workload, not the default 𝗚𝗲𝗻𝗔𝗜 & 𝗟𝗟𝗠 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 → Production RAG architecture: GPT\-4 \+ LangChain \+ CosmosDB vector search, chunking strategy, embedding pipeline, retrieval tuning — deployed at Experian at 2M\+ user scale → Multi\-agent orchestration: LangChain \+ CrewAI \+ Model Context Protocol \(MCP\) skill framework enabling 50\+ engineers to ship GenAI features 3× faster → LLM fine\-tuning: supervised fine\-tuning on domain\-specific B2B sales data with prompt engineering and structured output design → Edge AI: SNPE HTP operator implementation on Snapdragon NPUs — automated quantization debugger saving 50\+ engineering hours per week 𝗪𝗵𝗮𝘁 𝘀𝗲𝘁𝘀 𝗺𝗲 𝗮𝗽𝗮𝗿𝘁 I've worked at every layer of the stack — from writing C\+\+ operators on Snapdragon silicon to deploying GPT\-4 pipelines to millions of users\. That full\-stack depth means I don't just build the AI feature; I own the infrastructure that makes it reliable, observable, and cost\-efficient in production\. I treat every inference endpoint as a backend API first — with rate limiting, retry logic, monitoring, and SLO accountability\. 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 M\.E\. in Computer Science — Jadavpur University \(GPA: 8\.83\) B\.E\. in Computer Science — GCETT Berhampore \(GPA: 9\.02\) I'm actively exploring Senior Backend Engineer, Senior GenAI Engineer, and AI Engineer roles at product\-first companies — especially those scaling LLM\-powered systems, agentic infrastructure, or distributed backend platforms\. If your team is building something in that space, I'd love to connect\. 📩 mondalayan96ju@gmail\.com

## Location

West Bengal

## AI stack

codex

claudecode

## Work experience

Senior Software Engineer at Qualcomm, Apr 2024 \- Present · 2 yrs 5 mos, Hyderabad, \- Architected an 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗹𝗼𝗴\-𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘀𝘆𝘀𝘁𝗲𝗺 using LangChain and a proprietary LLM to auto\-diagnose CI build failures and recommend resolutions, 𝗰𝘂𝘁𝘁𝗶𝗻𝗴 𝗱𝗲𝗯𝘂𝗴 𝘁𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 𝗯𝘆 𝟲𝟬% for 300\+ engineers globally\. \- Built an 𝗠𝗖𝗣\-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝘀𝗲𝗹𝗳\-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗴𝗲𝗻𝘁 on top of Release Confluence and MongoDB, enabling natural\-language queries over internal release data — 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗰𝗿𝗼𝘀𝘀\-𝘁𝗲𝗮𝗺 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗯𝘆 𝟮𝟱% and eliminating ad\-hoc data requests across engineering orgs\. \- Migrated 𝟭𝟱𝟬\+ 𝗳𝗿𝗲𝗲𝘀𝘁𝘆𝗹𝗲 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 𝗷𝗼𝗯𝘀 to declarative pipelines with standardised templates, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗿𝗿𝗼𝗿𝘀 𝗯𝘆 𝟰𝟬% and establishing CI/CD standards adopted org\-wide\. \- Designed an 𝗠𝗖𝗣 𝘀𝗸𝗶𝗹𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 enabling 50\+ engineers to ship GenAI features 𝟯× 𝗳𝗮𝘀𝘁𝗲𝗿 using reusable LLM\-backed tooling — cutting average feature delivery from weeks to days\.

Software Engineer at Experian, Dec 2023 \- Apr 2024 · 5 mos, Hyderabad, \- Architected and deployed a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗔𝗚 𝗰𝗵𝗮𝘁𝗯𝗼𝘁 \(GPT\-4 \+ LangChain \+ CosmosDB vector search\) explaining credit\-score changes in plain language — shipped to 𝟮𝗠\+ 𝘂𝘀𝗲𝗿𝘀 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟲 𝘄𝗲𝗲𝗸𝘀 with 𝟵𝟮% 𝘂𝘀𝗲𝗿 𝘀𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 at launch\. \- Standardised 𝟮𝟬\+ 𝗺𝗼𝗱𝗲𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 via AWS SageMaker with MLflow monitoring, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗯𝘆 𝟯𝟱% 𝗮𝗻𝗱 𝗟𝗟𝗠 𝗔𝗣𝗜 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝟯𝟬% across all production pipelines\.

Data Scientist at Outplay, Jun 2023 \- Aug 2023 · 3 mos, Hyderabad, \- Deployed an 𝗔𝗜 𝗘𝗺𝗮𝗶𝗹 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 \(fine\-tuned GPT \+ RAG pipeline\) across 500\+ B2B sales teams, 𝘁𝗿𝗶𝗽𝗹𝗶𝗻𝗴 𝗦𝗗𝗥 𝗿𝗲𝗽𝗹𝘆 𝗿𝗮𝘁𝗲𝘀 𝗳𝗿𝗼𝗺 𝟭𝟱% → 𝟰𝟱% — directly accelerating revenue pipeline for every customer account\. \- Built an 𝗶𝗻\-𝗵𝗼𝘂𝘀𝗲 𝗔𝗦𝗥 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗶𝘁𝗵 𝘀𝗽𝗲𝗮𝗸𝗲𝗿 𝗱𝗶𝗮𝗿𝗶𝘀𝗮𝘁𝗶𝗼𝗻 from scratch in approximately 1 month, replacing a third\-party vendor and 𝗰𝘂𝘁𝘁𝗶𝗻𝗴 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝟱𝟬% \($80K → $40K/yr\) at 95% accuracy\. \- Developed 𝗕𝗘𝗥𝗧\-𝗯𝗮𝘀𝗲𝗱 𝘁𝗼𝗻𝗲\-𝗮𝗱𝗷𝘂𝘀𝘁𝗺𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 with an A/B testing framework, 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗲𝗺𝗮𝗶𝗹 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗿𝗮𝘁𝗲𝘀 𝗯𝘆 𝟮𝟬% across live customer cohorts\.

Associate Data Scientist at Outplay, Jul 2022 \- May 2023 · 11 mos, Hyderabad, Telangana, India

AI/ML Engineer at Qualcomm, Jul 2021 \- Jul 2022 · 1 yr 1 mo, Hyderabad, \- Enabled 𝟯𝟬\+ 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹𝘀 on Snapdragon devices by implementing ONEHOT, LSTM, and Detection Out operators in 𝗦𝗡𝗣𝗘 𝗛𝗧𝗣 — directly unblocking 𝗺𝗼𝗯𝗶𝗹𝗲 𝗲𝗱𝗴𝗲\-𝗔𝗜 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 across multiple product lines\. \- Built an 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘁𝗼𝗼𝗹 eliminating manual trial\-and\-error across SNPE configurations, 𝘀𝗮𝘃𝗶𝗻𝗴 𝟱𝟬\+ 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗵𝗼𝘂𝗿𝘀 𝗽𝗲𝗿 𝘄𝗲𝗲𝗸 for the entire SNPE team\. \- Delivered an 𝗡𝗟𝗣\-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝗽𝗮𝗿𝘀𝗲𝗿 \(BERT \+ spaCy \+ HuggingFace\) adopted as the 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝘁𝗼𝗼𝗹 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟬\+ 𝘁𝗲𝗮𝗺𝘀, screening 100\+ candidates per month and saving 10\+ HR hours per week organisation\-wide\. \- Engineered an 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗝𝗜𝗥𝗔 𝗵𝗮𝗻𝗱𝗹𝗲𝗿 using BERT\-based NLP for intelligent issue routing, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗺𝗮𝗻𝘂𝗮𝗹 𝗶𝘀𝘀𝘂𝗲\-𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗲𝗳𝗳𝗼𝗿𝘁 𝗯𝘆 𝟳𝟬% and accelerating triage velocity across engineering orgs\.

Data Scientist at BarRaiser, Mar 2021 \- Jun 2021 · 4 mos, Bangalore Urban, \- Solely architected and delivered 𝟯 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 within a 4\-month internship — video analysis, anomaly detection, and tag suggestion — each deployed on AWS end\-to\-end\. \- Built an 𝗔𝗪𝗦 𝗘𝗹𝗮𝘀𝘁𝗶𝗰 𝗕𝗲𝗮𝗻𝘀𝘁𝗮𝗹𝗸 𝘃𝗶𝗱𝗲𝗼\-𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 processing Zoom recordings end\-to\-end, analysing 𝟱𝟬𝟬\+ 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗽𝗲𝗿 𝗺𝗼𝗻𝘁𝗵 with 90% question\-extraction accuracy — eliminating manual QA review entirely\. \- Architected 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 supporting 𝟭,𝟬𝟬𝟬\+ 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 with sub\-second response times providing the production backbone for real\-time interview analysis at scale\. \- Engineered an 𝗮𝗻𝗼𝗺𝗮𝗹𝘆\-𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 identifying score\-comment mismatches in interview feedback data, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗿𝗲𝘃𝗶𝗲𝘄𝗲𝗿 𝗲𝗿𝗿𝗼𝗿 𝗯𝘆 𝟯𝟱% and cutting QA analysis time across all interviewer performance records\. \- Developed a 𝗕𝗘𝗥𝗧\-𝗯𝗮𝘀𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻\-𝘁𝗮𝗴 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗺𝗮𝗻𝘂𝗮𝗹 𝘁𝗮𝗴𝗴𝗶𝗻𝗴 𝗲𝗳𝗳𝗼𝗿𝘁 𝗯𝘆 𝟲𝟬% and improving insight accuracy across the interviewer scoring pipeline\.

## Education

Master of Technology \- MTech, computer science and engineering, Jadavpur University

Bachelor of Technology \- BTech, Computer Engineering, Government College of Engineering and Textile Technology, Berhampore

[View the original profile](https://tal.club/meet/ayan-mondal-1)

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