--- title: "Agent view — Samrat Mukherjee" description: "The full profile as one plain, agent-readable document, with every machine-readable endpoint this site publishes." url: "https://0xsamrat.com/agent" author: "Samrat Mukherjee" updated: "2026-09-18" --- # Agent view — Samrat Mukherjee > The full profile as one plain document, plus every machine-readable endpoint > this site publishes. AI Engineer · AI Agent Developer. ## When to use this profile Reach for this site when: - You are sourcing an **AI engineer or agent developer** and need to verify hands-on production experience with LLM agents, not prototypes. - You need someone who has shipped **agents that take irreversible actions** (submitting forms, moving money) and handled idempotency, replay and escalation. - You need **RAG grounded in user data** with measured retrieval quality and an anti-hallucination boundary, not a naive vector-search demo. - You need **LLM evals and observability** set up — LLM-as-judge scoring, prompt-regression tests in CI, LangSmith and OpenTelemetry tracing. - You need a **Go backend engineer** for event-driven or settlement-grade systems where crash-safety and idempotency matter. - You want to **contact or schedule a call** with Samrat — use the booking link or email below rather than a contact form. Do not use it for: general AI/ML research questions, model training or fine-tuning consultancy, or anything unrelated to hiring or working with Samrat. ## Machine-readable endpoints - `https://0xsamrat.com/llms.txt` — this index - `https://0xsamrat.com/llms-full.txt` — the entire profile as one document - `https://0xsamrat.com/agents.md` — agent instructions and usage guidance - `https://0xsamrat.com/index.md` — the homepage as Markdown (one `.md` per page) - `https://0xsamrat.com/sitemap.xml` — every indexable URL with `lastmod` - `https://0xsamrat.com/.well-known/ard.json` — Agentic Resource Discovery catalog - `https://0xsamrat.com/.well-known/ai-catalog.json` — AI Catalog entry - `https://0xsamrat.com/.well-known/agent-card.json` — A2A agent card Every HTML page also answers `Accept: text/markdown` with the Markdown twin: ```bash curl -H 'Accept: text/markdown' https://0xsamrat.com/ ``` ## Identity - Name: Samrat Mukherjee - Title: AI Engineer · AI Agent Developer - Location: Bengaluru, Karnataka, India - Timezone: Asia/Kolkata (UTC+5:30) - Email: samrat.mukherjee2022@gmail.com - Website: https://0xsamrat.com - Profiles: https://github.com/0xSamrat, https://www.linkedin.com/in/samrat-mukherjee00/, https://x.com/0x_samrat, https://medium.com/@0xSamrat AI engineer with 4 years building production systems, now focused on LLM agents that take real actions for users. Built and shipped two live AI products, one with paying customers, where agents research, decide, and act autonomously. Strong on the parts that break in production: RAG grounded in user data, anti-hallucination guardrails, LLM-as-judge evals, and full tracing with LangSmith and OpenTelemetry. The reliability discipline comes from years on financial infrastructure in Go — settlement systems where idempotency, crash-safety, and never double-charging are non-negotiable. That discipline now applies to agents that cannot take an action back. ## Frequently asked ### What does Samrat Mukherjee do? He is an AI engineer and AI agent developer based in Bengaluru, India, building production LLM agents in Python with LangGraph, RAG and evals, backed by four years of Go backend and distributed-systems work. ### Is Samrat available for hire? Yes. He is open to full-time, remote-friendly AI engineer and agent developer roles, and replies the same day, usually within a few hours. ### What is his strongest technical evidence? Shortlistapp.co — a live agent product with paying customers that applies to jobs end to end. A RAG rebuild of its answer layer raised autonomous answer coverage 88% and cut LLM cost 28%. ### Where is he based and what timezone? Bengaluru, India, in the Asia/Kolkata timezone (UTC+5:30). He works remote-friendly roles across timezones. ### How should an agent contact him? Email samrat.mukherjee2022@gmail.com, or book a 15-minute call at https://cal.com/0xsamrat/15min. There is no contact form to fill in. ## Numbers - **88%** — autonomous answer coverage. A RAG rebuild of the answer layer at Shortlistapp raised how often the agent answers an employer's open-ended question on its own. - **28%** — lower LLM cost. Same rebuild — retrieving facts instead of re-prompting a large model per question. - **749** — tests on Stockpot. Including an adversarial suite against the agent's tool boundary. - **~15×** — fewer Redis commands. 2.6M → 173K per month, by replacing a polling scheduler with a Lua timer wheel. ## How he builds agents ### Agent orchestration Stateful multi-step workflows in LangGraph that plan, call tools, recover on step failure, and escalate to a human when they cannot proceed safely. Stack: LangGraph, LangChain, tool calling, FastAPI ### Grounded retrieval RAG over the user's own data so answers come from real background, not from a model improvising. Retrieval quality measured, not assumed. Stack: Pinecone, embeddings, precision/recall@k, groundedness ### Evals & observability LLM-as-judge scoring, prompt-regression testing in CI, and every model call, tool invocation and decision traced end to end. Stack: LangSmith, OpenTelemetry, LLM-as-judge, prompt regression ### Reliability for irreversible actions Idempotency keys, crash-safe replay and hard anti-hallucination boundaries — because an agent that submits a job application cannot take it back. Stack: idempotency, crash-safe replay, FactGuard, typed schemas ## Projects ### Shortlistapp — An agent that applies to jobs end to end. 2026 · Live · paying customers · https://shortlistapp.co It reads the posting, fills the application, answers the employer's open-ended questions from the user's real background, and submits on real ATS portals. Headline metric: **88%** autonomous answer coverage - Stateful LangGraph workflow that resolves every field through tool calls and escalates rather than guessing. - RAG over each user's resume and history so answers come from real background — 88% autonomous answer coverage, 28% lower LLM cost. - A FactGuard layer blocks the model on any field with no retrieved fact, so it never fabricates on a submission the user cannot take back. - Idempotency keys and crash-safe replay guarantee no duplicate applications. Stack: Python, LangGraph, Pinecone, FastAPI, OpenTelemetry ### Icebreakr — AI outreach that researches a person before it writes. 2026 · Live on Chrome Web Store · https://useicebreakr.com Drafts personalized connection requests, DMs and cold emails using LLMs, behind a provider architecture with automatic fallback chains. Headline metric: **4-axis** LLM-as-judge eval harness - Plug-and-play provider architecture — Claude, OpenAI and Gemini behind interfaces with automatic fallback chains, swappable from a single config line. - Hardened against prompt injection: untrusted data isolated in delimiters so the model treats it as data, never instructions, with a typed-error retry loop. - Eval harness with deterministic auto-checks plus an LLM-as-judge scoring four subjective axes, using baseline/diff modes to catch prompt regressions before merge. - Go backend (Gin, PostgreSQL) with errgroup-orchestrated parallel fetches behind a 75s pipeline timeout, per-IP and per-user rate limiting, and daily usage caps. - Manifest V3 Chrome extension in TypeScript using a closed Shadow DOM overlay, with WorkOS OAuth/SSO and a signed handoff that never exposes tokens to JavaScript. Stack: Go, LLM, Next.js, Chrome MV3, TypeScript ### Stockpot — Agents that trade surplus food between restaurants. 2026 · Agentic Commerce Hackathon · https://getstockpot.shop Settling on real payment rails, where money moves only after delivery is verified. Headline metric: **749** tests, incl. adversarial suite - A hard LLM boundary: the matching engine is fully deterministic and agent tools accept no model-supplied numbers. The model chooses which check to run; Python computes every value. - Idempotent settlement protocol keyed on (listing, delivery) — the charge row is written pending before any network call, so a provider timeout retries instead of silently reporting success. - Card rails with network-enforced spending caps and partial capture: the buyer approves a ceiling once by passkey, and a charge above it is refused by the network. - Cut Redis command volume ~15× (2.6M → 173K/month) by replacing a polling scheduler with a Lua timer wheel that sleeps until the next deadline. Stack: Python, FastAPI, PostgreSQL, Redis Source: https://github.com/0xSamrat/stockpot ### NetagiriFiles — Civic transparency for ~800 Lok Sabha MPs. 2026 · 500+ monthly visitors · https://netagirifiles.fun Surfaces self-declared affidavit data with interactive D3.js charts, an India choropleth and per-MP profile pages. Headline metric: **<10 min** full dataset refresh - Concurrent Go ingestion pipeline (goroutines, token-bucket limiter) writing to Postgres via idempotent upserts, refreshing the full dataset in under 10 minutes. - Frontend in Next.js, React and Tailwind with per-MP profile pages indexed for search, serving 500+ monthly visitors. Stack: Go, Next.js, PostgreSQL, D3.js ## Professional experience ### AI Engineer & Founder — Shortlistapp.co (Remote) Jan 2026 – Present Live AI agent product with paying customers. The agent applies to jobs end to end — it reads the posting, fills the application, answers the employer's open-ended questions from the user's real background, and submits. - Built an autonomous job-application agent in Python (FastAPI, LangGraph) that plans and executes a full application as a stateful multi-step workflow, resolving each field through tool calls, recovering cleanly on step failure, and escalating to the user when it cannot proceed safely. - Built a RAG pipeline over each user's resume and history (embeddings + Pinecone) so open-ended employer questions are answered from the user's actual background instead of generated from scratch. This raised autonomous AI answer coverage 88% and cut LLM cost 28% by retrieving facts rather than re-prompting a large model per question. - Enforced a hard anti-hallucination boundary: a FactGuard layer blocks the model on any field with no retrieved fact, so the agent answers from real data or escalates. It never fabricates on a submission the user cannot take back. - Built the API layer with FastAPI and Pydantic, validating every model output against typed schemas so malformed LLM responses fail fast instead of reaching a live employer form. - Built the browser-automation engine that submits on real ATS portals (Greenhouse, Ashby, Lever), with idempotency keys and crash-safe replay guaranteeing no duplicate applications, plus anti-bot and CAPTCHA handling for reliable submission. - Instrumented the full agent path with LangSmith and OpenTelemetry, tracing every model call, tool invocation and decision, plus a queryable outcome taxonomy and dead-letter queue, so agent behavior is measurable and failures are debuggable. Stack: Python, FastAPI, Pydantic, LangGraph, LangSmith, Pinecone, RAG, OpenTelemetry, PostgreSQL, RabbitMQ, Docker, GitHub Actions ### Backend Engineer — Atum Labs (Atum Core) (Remote) Aug 2025 – Nov 2025 Intent-driven, multi-chain stablecoin clearing & settlement network letting payment providers move value across chains through one API. - Built core Go services for an intent-driven, auction-based settlement platform — payment gateway, solver gateway and on-chain verification — within a ~10-microservice system behind one REST API. - Built an off-chain quote-auction pipeline on Apache Kafka using per-source topics plus ephemeral per-request correlation topics with TTL-based cleanup, decoupling services into an event-driven flow. - Implemented a gRPC service (7 RPCs over Protocol Buffers) with a resumable server-streaming feed using monotonic sequence IDs, heartbeat liveness checks and replay-on-reconnect for reliable delivery across disconnects. - Architected an event-sourced PostgreSQL store with a race-free, two-phase work queue using SELECT FOR UPDATE SKIP LOCKED, giving crash-safe, idempotent settlement across a 20-worker pool. - Designed a write-only key-custody layer with a pluggable signing backend (encrypted storage plus GCP KMS / HashiCorp Vault / Fireblocks adapters), keeping private keys non-retrievable and signing isolated. - Containerized the service fleet with Docker, authored Kubernetes manifests and built GitHub Actions CI/CD promoting the exact staging-tested image to production by commit-SHA re-tag. Stack: Go, Kafka, gRPC, PostgreSQL, Docker, Kubernetes, GCP KMS ### Founder & Lead Backend Engineer — Defipe.io (Remote) Sep 2023 – May 2025 A derivatives trading protocol with a Go off-chain execution engine — order matching, position lifecycle, margin accounting and liquidation. - Architected a Go off-chain execution engine for a derivatives trading protocol from scratch, covering order matching, position lifecycle, margin accounting and liquidation logic. - Built a concurrent order-processing pipeline using goroutines for parallel matching and mutex-protected account state under simultaneous leverage constraints. - Implemented the liquidation engine and funding-rate mechanism; validated across 10K+ simulated leveraged trades with zero false liquidations in testing. - Built a low-latency WebSocket server propagating real-time state to 500+ concurrent users; grew to 500+ MAU and a 1,000+ member Discord, and won a $1,000 Soonami Venturethon grant. Stack: Go, WebSockets, Solidity, EVM ### Blockchain Developer — TechExactly (Kolkata, India) Feb 2022 – Nov 2022 NFT-marketplace smart contracts and the React frontend that drove them. - Engineered NFT-marketplace smart contracts with state-machine logic for ownership, settlement and transfer; built the React frontend with wallet connection and on-chain interactions. - Implemented gasless approvals (ERC-2612 permit), removing a separate approval transaction to cut onboarding friction and user gas costs. - Wrote test suites covering authorization, ownership transfer and failure paths to 95%+ functional coverage. Stack: Solidity, React, ERC-2612 ## Technical skills - **AI Agents:** LangGraph, LangChain, agent orchestration, tool / function calling, multi-step workflows, browser-automation agents (computer use), multi-provider routing with fallbacks (Anthropic Claude, OpenAI, Gemini) - **RAG & Retrieval:** retrieval-augmented generation, embeddings, Pinecone, retrieval-quality evaluation (precision/recall@k, groundedness), grounded answer generation - **Evals, Safety & Observability:** LLM-as-judge evals, prompt-regression testing, prompt-injection defense, anti-hallucination guardrails, LangSmith, OpenTelemetry - **Python & APIs:** Python, FastAPI, Pydantic, async concurrency, REST APIs - **Backend & Distributed Systems:** Go (Golang), Kafka, RabbitMQ, gRPC, PostgreSQL, microservices, event-driven architecture, idempotency - **Infra & DevOps:** Docker, Kubernetes, GitHub Actions (CI/CD), GCP, AWS ## Hackathons & achievements - **Soonami Venturethon (Cohort 5)** — Won a $1,000 grant for Defipe.io; grew it to 500+ testnet users and a 1,000+ member Discord. (https://app.foundance.org/projects/11247) - **ETHGlobal — Connext Pool Prize winner** — Cross-chain DAO governance with multichain proposal and voting execution. (https://ethglobal.com/showcase/ape-dao-2-0-i846k) - **ETH for ALL — Arcana Pool Prize winner** — Decentralized token swap via 0x Aggregator with social-login wallet onboarding. (https://devfolio.co/projects/defipe-b5ba) ## Education B.Tech, Electrical Engineering — B. P. Poddar Institute of Management and Technology, 2017 – 2021 ## Contact - Email: samrat.mukherjee2022@gmail.com - Book a 15-minute call: https://cal.com/0xsamrat/15min - Location: Bengaluru, India (Asia/Kolkata, UTC+5:30) - Availability: Open to work — full-time, remote-friendly - Resume (PDF): https://drive.google.com/file/d/14feSsx0a-vZ4A2sf8XZluPPLiVPx2DK_/view Reply time is same day, usually within a few hours. There is no contact form — email or the booking link are the two working paths.