Senior Software Engineer with 10+ years building scalable data pipelines, cloud-native infrastructure, and enterprise backend systems. Currently working on indexing and searching petabytes of M365 data.

🌐 hiteshpattanayak.com · AWS Community Builder · CKAD Certified

Languages: Go · Python · TypeScript / Node.js · PySpark
Data Engineering: Databricks · Apache Spark · Delta Lake · Azure Event Hubs
Cloud & Infra: Kubernetes · Docker · Azure · AWS · Terraform · Pulumi
Databases: CosmosDB · PostgreSQL · Elasticsearch · TimescaleDB
AI / LLM: RAG pipelines · Azure OpenAI · Anthropic API · Vector Search
Protocols & APIs: gRPC · REST · GraphQL

• Ultimate CKAD Certification Guide — OrangeAva
• Modern API Design with gRPC — OrangeAva

• Flash talk — gRPC Load Balancing @ GopherCon 2023
• Virtual talk — Microservice Communication using gRPC @ AWS UG Bangalore
• Blog featured in kube-weekly
• Blog featured in LearnK8s LinkedIn pulse

• On-call Copilot — Claude Code plugin system covering 30+ alert types across data pipeline services; paste an alert URL, get root cause analysis and remediation in under 2 minutes; 100% team adoption
• Semantic Search (RAG) — CosmosDB hybrid vector search + Azure OpenAI over petabytes of M365 backup data; natural language → metadata filters via few-shot Chat Completions
• Elastic Dashboard Changelog — Python + Anthropic API tool that diffs unreadable .ndjson Kibana files and generates human-readable changelogs
• Security Fix Automation — LLM-assisted local skill that ingests Cycode findings and applies targeted fixes with full code context
• Blog Generator — AI-powered workflow (Claude / OpenAI) to draft posts from structured idea files
• AI Chat Assistant — RAG conversational assistant on my blog site (TF-IDF + Netlify Functions + GPT-4o-mini)

My blog has a built-in AI chat assistant. Ask it about my posts, projects, or background — it retrieves relevant content and answers using GPT-4o-mini.

👉 Chat at hiteshpattanayak.com

I’m exploring the integration of gRPC with Kubernetes to enhance the efficiency of data pipelines, particularly focusing on streaming AI/LLM outputs through Go services. Additionally, I'm experimenting with Azure's Databricks for implementing retrieval-augmented generation (RAG) strategies, using OpenAI and Anthropic APIs to improve the intelligence of our applications. This combination of technologies is aimed at reducing latency and improving data accuracy across our cloud-native systems.

_{Powered by Claude via scheduled GitHub Actions · view workflow}