Jeethesh avatar

Hi, I'm Jeethesh,

Software / AI Engineer
who ships distributed systems with
Evals + Grounding + Ops

My path

Work experience

End-to-end ownership across discovery → delivery → adoption, collaborating closely with stakeholders and engineering.

Enterprise OSDU publishing

Data transfer of multiple data products to OSDU

AI-assisted quality engineering

Dual-agent Stryker loops, ≥95% coverage, reusable across 10+ teams

Kafka platform reliability

Recovered capacity at 18,000 partitions and diagnosed the 4,000-ACL ceiling

Event-driven orchestration

Node.js, Kafka, Redis, DSDT transfers, and idempotent job-state workflows

Realtime transfer visibility

Kafka → Socket.IO status delivery scoped by well, scenario, and tenant

Compliance & observability

New Relic tracing, structured logs, activity audits, and technical-status notifications

Halliburton

Software Engineer

July 2024Present

Houston, TX

  1. Built a real-time data publishing platform enabling users to select, fetch, and transfer company data to the OSDU domain via Node.js microservices, Kafka messaging, Redis caching, WebSockets, and Angular UI enabling secure cross-domain data transfer for 7 transfer entities with 15–20 second latency on repeat transfers.

  2. Built dual-agent framework generating unit tests to ≥95% coverage and validating test quality with Stryker mutation testing across 10+ team repositories, enabling high-confidence trunk-based development with daily merges and reducing QA delays.

  3. Unblocked topic creation for multiple teams by resolving enterprise Kafka deployment constraints identified and cleaned 200+ stale feature-branch topics, resolved Confluent limits at 18,000 partitions and 4,000 ACLs.

  4. Accelerated CI feedback by implementing incremental Stryker validation across 10+ team repositories, adding baseline file protection checks and automated artifact-to-commit workflows with CI token permissions.

  5. Enabled new customer features by building data retrieval pipeline that ingests 4 external company data products from OSDU domain into internal systems, unlocking access to third-party data for engineering calculations.

  6. Integrated New Relic, structured logging, activity auditing, and Kafka technical status notifications for end-to-end observability.

What I build

Projects

A retrieval-augmented assistant over personal resilience notes — Flask orchestrates Gemini + Pinecone with Cohere reranking, citation-grounded answers, Firebase auth, and Cloud Run deployment.

Why I built it

I wanted to stay consistent through personal challenges, but generic habit trackers never felt personal enough to stick. Building one around my own reflection process meant I could shape it to how I actually think — and feel invested enough to keep using it.

  • 30/30 golden CI tests and 6/6 live RAGAS evals (1.0 faithfulness, 0.90 relevancy)
  • Intent-aware routing that bypasses vector search for aggregate queries
  • Langfuse per-stage tracing for retrieval and generation failures
  • Per-user rate limiting and server-side secret management on Cloud Run

30/30

Golden tests

1.0

Faithfulness

0.90

Relevancy

Stack

GeminiPineconeCohereFlaskFirebaseCloud RunLangfuseRAGASReact

Education

Academic foundation

Data science graduate paired with a strong engineering undergraduate core.

University of North Texas

M.S., Data Science

Aug 2022 – May 2024 · GPA 3.9/4.0

National Institute of Technology Jamshedpur

B.Tech., Electronics & Communication Engineering

July 2018 – May 2022 · GPA 7.6/10.0

Capabilities

Skills

Systems, AI, cloud, and backend craft — grouped by how they show up in shipped work.

Languages

EnglishProfessional proficiency
TeluguNative
HindiProfessional proficiency
SpanishStill buffering…

Distributed Systems

  • Node.js microservices
  • Kafka / Confluent
  • Redis
  • WebSockets / Socket.IO
  • Idempotency & job state
  • Observability (New Relic, Sentry)

AI / LLM

  • RAG & vector search
  • Embeddings & reranking
  • LLM evaluation (RAGAS)
  • Prompt engineering
  • Observability (Langfuse)
  • Structured outputs
  • AI agents & mutation testing
  • Local LLM inference

Cloud & DevOps

  • Google Cloud Run
  • Firebase
  • Docker / Kubernetes / Helm
  • Azure DevOps
  • GitHub Actions / Jenkins
  • CI/CD

Backend & Languages

  • Python
  • TypeScript / JavaScript
  • C# / Java / SQL
  • Flask / FastAPI
  • Express / Hapi.js
  • Angular / Next.js / React
  • PostgreSQL / Supabase / Pinecone / SQLServer

Credentials

Certifications

Signals of continued learning across AI tooling and modern developer workflows.

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