Open to Senior AI/ML & Applied AI roles

I build production AI systems that hold up under real load.

I’m Ankit Gautam, a Senior AI/ML Engineer owning LLM systems end-to-end — FastAPI service layers, LangGraph agents, citation-grounded RAG, and the evaluation harnesses that gate every release.

AI AgentsLLM SystemsApplied AIRAGMLOpsBackend EngineeringProduction AI

gabbarx · online

Ankit Gautam

Senior AI/ML Engineer

10x

engineer

self-reported

flaky tests I admit to

CI disagrees

3am

best ideas ship

regret by 9am

∞

coffee → commits

caffeine-driven dev

scroll

Featured Work

Production AI systems, end to end

Case studies in shipping RAG pipelines, agent workflows, and the infrastructure that keeps them reliable. Expand any card for the engineering decisions behind it.

RAGLLMBackendInfrastructure

AWS-Deployed Production RAG API

Production RAG service on ECS Fargate serving live retrieval + generation.

2026

API Gateway / ALB

FastAPI service (ECS Fargate, autoscaled)

Retrieval: OpenSearch + pgvector hybrid

Embedding cache + async top-k rerank

S3 document store · CloudWatch observability

A citation-grounded Retrieval-Augmented Generation API deployed on AWS ECS Fargate with REST endpoints for ingestion, chunking, embedding, retrieval, and grounded generation over 120K+ document chunks.

4.8s → 1.9s

P50 latency

120K+

Doc chunks

API-key + request validation

Auth

RAGAgentsLLMAI

Graph-RAG Knowledge Assistant

Hybrid vector + knowledge-graph retrieval for multi-hop reasoning.

2025

LangGraph orchestrator (classify → retrieve → traverse → synthesize)

Semantic vector retrieval

Neo4j graph expansion (Cypher)

Graph-constrained context validation

Dockerized FastAPI backend

A knowledge assistant combining semantic vector search with Neo4j knowledge-graph traversal, orchestrated through LangGraph, for multi-hop reasoning over tightly interconnected documents.

61% → 84%

Multi-hop QA

75K+

Entities indexed

210K+

Relationships

AgentsLLMRAGMLOps

EcoRatings Agentic AI Platform

Dockerized FastAPI AI services on AWS with agentic ESG workflows.

2026

Fallback model router (OpenAI / Anthropic / open-weight)

LangGraph agents: tools · state · validation · human review

Hybrid retrieval: pgvector + Chroma + rerank

ECS Fargate autoscaling · CloudWatch monitoring

LLM evaluation harness gating releases

Production AI services powering ESG analysis: citation-grounded RAG over domain documents plus LangGraph multi-step agentic workflows with tool execution, validation loops, and human-review checkpoints.

Providers routed

Automated evals

Release gate

Autoscaled Fargate

Deploy

BackendInfrastructureFull StackMLOps

GCP Event-Driven Backend Platform

Cloud Run services + Pub/Sub pipelines with production observability.

2025

Cloud Run services (autoscaled, versioned revisions)

REST APIs: authz · validation · pagination · error handling

Async: Cloud Functions + Pub/Sub + scheduled jobs

Cloud SQL (Postgres/MySQL) — indexed + tuned

IAM + Secret Manager · Cloud Logging/Monitoring/alerts

Production backend services on GCP: FastAPI/Django on Cloud Run with autoscaling and versioned revisions, plus async event-driven processing via Cloud Functions, Pub/Sub, and scheduled jobs.

Event-driven

Processing

IAM + Secret Manager

Config

Logs + alerts

Observability

Career Log

Replaying the build history

Scroll to move through each role. The active chapter expands into the systems shipped, the decisions made, and what they taught.

Senior AI/ML Engineer

EcoRatings

Apr 2026 — Presentnow

Own production LLM systems end-to-end — from FastAPI service layers through LangGraph agent orchestration to Dockerized AWS deployment, monitoring, and evaluation.

Shipped

Architected and deployed Dockerized FastAPI AI services on AWS ECS Fargate with autoscaling, health checks, and CloudWatch monitoring, serving live RAG and agent workloads.
Built citation-grounded RAG pipelines over domain documents using pgvector + Chroma with hybrid retrieval, metadata filtering, and reranking, backed by S3 and AWS ingestion workflows.
Designed LangGraph multi-step agentic workflows with tool execution, state management, validation loops, and human-review checkpoints.
Built LLM evaluation harnesses measuring correctness, retrieval quality, hallucination risk, and schema adherence to gate releases.

Decisions

Fallback model routing across OpenAI, Anthropic, and open-weight models to balance cost, latency, and accuracy.
Hardened services with API auth, throttling, retries, timeouts, and schema validation.
Made eval harnesses a release gate to catch regressions before deploy.

Lessons

Production AI reliability is mostly disciplined software engineering — routing, retries, and evals matter more than model choice.
Human-review checkpoints are cheap insurance for high-stakes agent actions.

FastAPILangGraphpgvectorChromaAWS ECS FargateS3CloudWatchOpenAIAnthropic

Software Engineer

JaiwebSoft Technologies

Jul 2025 — Apr 2026

Built and deployed production backend services on GCP with FastAPI, Django, Docker, and Cloud Run — including async event-driven pipelines and hardened production configuration.

Shipped

Deployed backend services on Cloud Run with autoscaling, health checks, and versioned revisions.
Designed REST APIs with authentication, authorization, validation, pagination, and structured error handling.
Modelled and optimized PostgreSQL/MySQL schemas on Cloud SQL — indexing, migrations, and query tuning.
Built async processing with Cloud Functions, Pub/Sub, and scheduled jobs for ingestion, sync, and notifications.

Decisions

Moved coupling-heavy work to event-driven Pub/Sub pipelines.
Centralized secrets in Secret Manager with least-privilege service accounts.
Added observability via Cloud Logging, Monitoring, and alerts.

Lessons

Event-driven decoupling pays off the moment ingestion volume becomes unpredictable.
Schema and index design decide whether a service scales gracefully or falls over.

FastAPIDjangoDockerCloud RunCloud SQLPub/SubPostgreSQLMySQL

B.Tech, CS (Artificial Intelligence)

IIIT Delhi

2021 — 2025

Computer Science with an Artificial Intelligence specialization — foundations in ML, systems, algorithms, and applied AI, alongside competitive programming.

Shipped