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.info · 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

• 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.info

I'm exploring the intricacies of optimizing retrieval-augmented generation (RAG) by implementing more efficient vector search algorithms and enhancing grounding techniques to improve contextual relevance in responses. I'm particularly focused on integrating page-aware AI chat features that leverage per-page context to deliver a more personalized user experience while reducing latency in data retrieval from vector databases. Additionally, I'm considering strategies for effective chunking of knowledge to balance speed and accuracy in response generation.

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