engineer:
name: "Harshit Rai"
focus: ["AI/ML Engineering", "LLMs & Agentic AI", "MLOps", "DevOps", "GitOps"]
currently_building:
- RAG & Advanced-RAG pipelines with hybrid retrieval + re-ranking
- Agentic AI systems with multi-provider LLM orchestration & MCP tool-calling
- Polyglot backends: Go (orchestration/IO) + Python (ML/inference)
- CI/CD & GitOps workflows: Jenkins β Docker β K8s β ArgoCD
philosophy: "Ship from scratch to production β no shortcuts, no hallucinations."
status: "Actively building. Actively shipping. Actively learning."I design and ship end-to-end AI systems β from model inference and retrieval pipelines to the infrastructure that deploys, monitors, and scales them. My work spans the full stack: LLMs, RAG/Advanced-RAG, Agentic AI, Deep Learning, MLOps, and cloud-native DevOps/GitOps, built with a polyglot toolkit of Python, Go, Rust, and TypeScript/JavaScript.
| Category | Stack |
|---|---|
| π€ AI / ML / DL |
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| 𧬠LLM / RAG / Agentic AI |
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| π MLOps |
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| π οΈ DevOps / CI-CD / GitOps |
|
| π» Languages |
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| βοΈ Cloud |
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Distributed document-intelligence platform for automated contract risk analysis.
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Autonomous agent for triaging and resolving DevOps support tickets.
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Research-grade RAG platform with hybrid retrieval and grounded generation.
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Developer intelligence platform with async job processing.
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π Explore the full build breakdowns, live demos, and case studies on my portfolio:
π Building β Job-application automation engine (Go, SerpApi, LLM relevance scoring, Gmail API)
π± Deepening β Advanced RAG (hybrid + rerank), Agentic AI orchestration, gRPC polyglot systems
π― Targeting β Full-Stack AI/ML, MLOps, AI Platform & DevOps Engineering roles
π¬ Ask me about β RAG pipelines, LLM infra, MLOps, Kubernetes/ArgoCD, Go + Python systems design
