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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
  • Specialized coursework across machine learning, NLP, and systems.
  • Top 4 finish in the Lumos BUIDL Hackathon among 30+ teams.
  • Codeforces rating 1352 with 300+ DSA problems solved.
Decisions
  • Leaned into applied AI and production systems over pure research.
  • Built algorithmic depth through competitive programming.
Lessons
  • Strong DSA fundamentals compound into better systems intuition.
  • Shipping beats theorizing — hackathons taught me to scope and deliver fast.
PythonJavaC++SQLMachine LearningNLP