---
title: "Subrata Mondal - Founding AI Engineer in Bengaluru | Tal"
description: "Meet Subrata Mondal, Founding AI Engineer | Agentic Systems, LangGraph & LLM Evals | 5 AI backends 0→1 solo | 170K-doc legal RAG serving 2,000+ users based in Bengaluru. AI stack includes antigravity, claude code, and codex. Explore public work links including github.com and youtube.com."
image: "https://tal.club/meet/subrata-mondal-1/share-image"
---

# Subrata Mondal on Tal

## Headline

Founding AI Engineer \| Agentic Systems, LangGraph & LLM Evals \| 5 AI backends 0→1 solo \| 170K\-doc legal RAG serving 2,000\+ users

## About

Founding AI Engineer at lawworld\.ai \(Aug 2024 to Jun 2026\)\. Sole AI and backend engineer on a 3\-person team, shipped 5 production backends 0→1 \(a distributed, microservices\-style platform\) across 3 architectural generations \(LangChain LCEL → LangGraph → 8 agent primitives hand\-built in async Python\) into an 8\-12\-engineer\-shaped product\. I know the internals of the agent frameworks your team runs \(LangGraph, CrewAI, AutoGen\), not just the API surface: tool registry, prompt assembly, the loop, retry/fallback, state, permissions, observability, eval gate\. Ran LangGraph in production 4 months, then built those 8 by hand\. 🏗️ What I shipped in production: 1\. Reliability — per\-page MongoDB checkpointing \+ Azure Service Bus choreography, at\-least\-once execution, zero data loss on crashes\. 7–8× Pass 1 wall\-clock collapse \(450–700s → 80–100s\) via asyncio\.gather \+ Semaphore\(6\)\. 2\. Scale — 170K\-document legal corpus, 2\-stage retrieval \(Atlas Vector Search ANN → Voyage rerank\-2\.5\), Perplexity\-style citations with a cited\-only filter \(\~30× fewer persisted sources\)\. Serving \~2,000 registered users since the Jan 2026 launch\. 3\. Security — multi\-tenant JWT auth with per\-request ownership gates that return 404, not 403, on unauthorized access, closing the enumeration side\-channel most teams miss\. 4\. Cost engineering — per\-call cost\-attribution middleware into MongoDB\. Hundreds of Acts/Rules processed, 100\.8M\+ tokens, $296\.75 total cost, 47\.6% cache hit rate\. 5\. Eval discipline — authored the eval\-gating contract \(ADR\-005\): recall@5, phrase\-overlap, citation\-accuracy thresholds as CI merge gates, written before a line of chat code\. ⚙️ Open\-source work: ▸ argus — permission\-gated multi\-agent research engine, 8 primitives built by hand\. Clears 7 of 8 metrics on a curated eval benchmark, $0\.149/turn unit economics\. ▸ bare\-agent — published, zero\-dependency agent runtime on PyPI\. 8 core primitives, bounded ReAct loop\. ▸ agents\-eval\-framework — DSPy Extractor\-Judge calibration harness\. Kappa 0\.12→0\.9261 across 5 iterations, 480\-trace golden set\. 🛠 Core stack: Python 3\.12\+ \(Async\) · FastAPI · Pydantic V2 · LiteLLM · MCP \(Tool Calling\) · DSPy · Docker · Kubernetes \+ KEDA · Kafka & Message Brokers \(Azure Service Bus\) · Redis · MongoDB Atlas · PostgreSQL/pgvector · Voyage AI · OpenTelemetry · Terraform · AWS/Azure

## Location

Bengaluru

## AI stack

antigravity

claude code

codex

langchain

langfuse

langgraph

## Work experience

Founding AI Engineer at lawworldai, Aug 2024 \- Jun 2026 · 1 yr 11 mos, Bengaluru, Karnataka, India, Architected and deployed deterministic agentic AI systems, focusing entirely on infrastructure reliability, evaluation metrics, and unit economics\. 🚀 Agentic Substrate & Orchestration: ▸ Built Primitive\-Level Agentic Orchestration: Architected the 8 core agentic primitives from scratch in async Python—including a three\-axis loop \(tokens, turns, time\)\. Achieved a 7–8× wall\-clock speedup \(700s → 100s\) on Pass 1 extraction via asyncio\.gather \+ Semaphore\(6\)\. ▸ Enforced Strict Data Contracts: Designed a multi\-agent swarm utilizing production tool\-calling with strict Pydantic V2 JSON validation to handle high\-entropy reasoning tasks\. ▸ Architected Durable Execution: Implemented MongoDB per\-turn checkpointing combined with Azure Service Bus choreography to guarantee at\-least\-once execution and zero data loss on node crashes\. 📊 Ops\-to\-Research Evaluation & FinOps: ▸ Engineered Evaluation Infrastructure: Designed an offline DSPy Extractor\-Judge evaluation graph that enforced a 0\.90 Cohen's Kappa agreement baseline against human golden datasets\. ▸ Deployed Autonomous GEPA Loops: Implemented self\-healing pipelines that autonomously rewrite agent instructions based on trace failures\. ▸ FinOps Cognitive Routing: Shipped a multi\-provider LiteLLM proxy router that dynamically shifts workloads based on cognitive demand\. Processed 15,775 pages pushing 100\.8M tokens with a 47\.6% prompt\-cache hit rate, achieving a total inference cost of just $296\.75\. 🛠 Substrate Stack: Python 3\.12\+ \(Async\) · FastAPI · Docker · Kubernetes \+ KEDA · Terraform · Redis · MongoDB Atlas · DBOS Durable Execution · LiteLLM · DSPy · Pydantic V2 · OpenTelemetry \(OTel\) · structlog · Model Context Protocol \(MCP\) · Azure/AWS

Python Developer at The AlgoHype, Jul 2024 \- Sep 2024 · 3 mos, New Delhi, Delhi, India, 3\-month engagement \(pre\-lawworld\.ai\) — built two production AI services: ▸ Synthetic data pipeline for LLM fine\-tuning — Azure OpenAI for synthetic Q&A pair generation against a domain corpus\. Document ingestion via python\-docx \+ pymongo, Pydantic schema validation \+ content\-relevance \+ duplicate\-detection quality gates, LangFuse for prompt tracing, backoff library for rate\-limit retry, Docker for deployment\. ▸ Context\-aware QA microservice on FastAPI \+ Docker — multi\-provider LLM routing \(OpenAI \+ Groq\) via LangChain, Qdrant vector DB for similarity search, Pydantic\-typed API contracts, LangFuse instrumentation, Azure Pipelines for CI/CD\. 🛠 Stack: Python, FastAPI, Docker, MongoDB, Pydantic, Qdrant, LangChain, LangFuse, OpenAI, Groq, Azure OpenAI, Azure Pipelines\.

Junior Machine Learning Engineer at Omdena, Nov 2023 \- Jan 2024 · 3 mos, Palo Alto, California, United States, 8\-week AI Innovation Challenge with Omdena \(community\-led, volunteer\-basis\): collaborated on an LLM\-powered interview\-prep chatbot — speech input, structured assessment, feedback loop\. NLP \+ RAG \+ prompt engineering\. Demo: https://drive\.google\.com/file/d/1EDcnxE53pu77E4FJsVt4BMqZIWb5WZMu/view

Data Analyst at Trainity, Apr 2023 \- Nov 2023 · 8 mos, Bengaluru, Karnataka, India, 8\-month part\-time structured training in data analysis \+ machine learning \(concurrent with final\-year BTech\)\. Capstone projects: customer\-churn prediction model, marketing\-campaign optimization analysis, interactive dashboard suite\. Stack: Python, SQL, Excel, Tableau\.

## Education

Bachelor of Technology \- BTech, Computer Science Engineering, Parul University

## Public links

- github\.com: https://github\.com/subratamondal1/argus
- youtube\.com: https://youtube\.com/@subratamondal1\-07

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

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