Hey, I'm @moonman369 Backend-focused Software Engineer with Web3 roots, now deep in the lab making large language models do useful things (ideally on purpose). I turn vague AI ideas into shipped, observable, low‑latency, "wow‑that‑actually‑works" systems — grounded in Java, Spring Boot, .NET, and Azure.

🌐 Portfolio · 💼 LinkedIn · 🧩 LeetCode (400+) · 🐙 GitHub

• 🤖 AI & GenAI – prompt alchemy → hardened pipelines (evals, guardrails, cost + latency dashboards).
• 🧩 AI Agents – planner / executor loops, tool orchestration, memory layers, self‑reflection (minus the existential crisis).
• 📚 RAG – hybrid retrieval (vector + keyword + rerank), smarter chunking, quality metrics > vibes.
• 🛰 MCP Architecture – Model Context Protocol as a clean contract between tools, data and agents.
• 🛠 AI-Guided Dev – spec-to-code, test synthesis, refactor assists, documentation autowriting (yes, I let the model explain my own code sometimes).
• 🔁 Feedback Loops – tracing prompts, grounding scores, hallucination audits, & tightening the iteration cycle.

• 💼 Systems Engineer (Azure Integration Developer) @ Tata Consultancy Services(Aug 2023 – Present)
  • Designed & deployed 20+ resilient microservices (Java Spring Boot + .NET) for a major UK retail client, optimized for concurrency and low latency.
  • Built a multi-agent AI orchestration prototype on Azure AI Foundry automating planning → code generation → docs → repo creation → cloud provisioning → deployment.
  • Shipped 10+ production GenAI prompts for automated code reviews, vulnerability analysis, and docs — cutting security vulnerabilities ~50% and lifting operational efficiency ~40%.
  • 5/5 appraisal, "A" band — 3 years running.
• 🧱 Software Engineer Intern (Web3) @ W3Dev Private Limited — architected full-stack dApps & smart contracts with React.js, Node/Express, and Ethers.js.
• 🏁 Chainlink Spring Hackathon 2023 participant — learned fast, shipped faster.
• 🔗 Crypto playground across: Ethereum, Polygon, Avalanche, Solana, Near, Aptos, Cosmos, Cardano.
• 🧪 Tooling comfort: Hardhat, Truffle, Metamask, Ethers.js, Web3.js, NearJS, etc.

• 🤖 MoonMind AI — deterministic RAG intelligence system with a multi-stage NLP pipeline (intent classification → hybrid retrieval → ranking) that turns static portfolios into queryable knowledge engines. Python · OpenAI LLMs · Qdrant.
• 🛒 BlinkMart — high-performance quick-commerce app with JWT auth, optimized product search, and payment gateway integration. MongoDB · Express · React · Node (MERN).

• 🧠 RAG‑Based Code Understanding: A retrieval layer that treats source code as a living knowledge graph — hybrid embeddings + structure-aware parsing (AST, symbols, dependencies) → precise, context-packed answers instead of fuzzy guesswork.
• 🏷 Intelligent Prompt Classification: Auto-routing user intent (debug? architectural advice? refactor? explanation?) to tailored system prompts, tools and safety rails — lower latency, higher signal, fewer wasted tokens.
• 🧾 Code Summarization Tool: Multi-resolution summaries (function → module → service) with freshness tracking + diff-aware regeneration; powering "what changed" briefs and onboarding snapshots.
• 🛰 MCP Server Architecture: A modular Model Context Protocol server exposing tools, datasets, retrieval endpoints and execution sandboxes as composable, observable capabilities for agents.

• Adaptive tool routing (semantic + cost‑aware).
• Vector DB benchmarking (recall vs MIPS vs memory footprint).
• Conversation memory blends (episodic snapshots + semantic embeddings).
• Execution sandboxes for semi‑autonomous agents (security > chaos).

• Web3 × AI intersections (verifiable inference, oracle‑enhanced reasoning).
• Knowledge graphs + embeddings alignment for richer entity grounding.
• Multi‑modal context (logs + code + docs fused for dev copilots).

• Oracle Cloud Infrastructure – GenAI Professional
• Azure Fundamentals (AZ-900)
• GenAI for Software Developers (IBM)
• IT Automation with Python (Google)

Ship small. Observe everything. Optimize hot paths. Document intent (so Future Me owes Present Me fewer apologies).

• Core Languages: Python, JavaScript/TypeScript, Go
• LLM Providers: OpenAI, Azure OpenAI, (exploring Anthropic / HF)
• Retrieval: FAISS, pgvector, hybrid BM25 + dense + rerank strategies
• RAG Craft: chunk heuristics, semantic filters, Hallucination vs Grounding eval sets
• Agents: planner–executor loops, tool routing, scratchpad refinement, structured outputs
• MCP: experimenting with Model Context Protocol as a clean tool/data contract
• Observability: prompt traces, latency histograms, cost per feature, eval dashboards
• Dev Assist: AI-driven test generation, refactor suggestions, auto‑doc pipelines

• Frontend: React, Next.js, Vue, Tailwind, HTML5, CSS3
• Backend: Node.js (Express), REST design, microservices (Spring Boot), .NET Framework
• Cloud: Azure (identity, services, deployment flows)
• Data: MongoDB, SQL fundamentals, schema iteration

• Smart Contracts: Solidity, Hardhat, Truffle
• Chains: Ethereum, Polygon, Avalanche, Solana, Near, Cosmos, Cardano, Aptos
• Tooling: Ethers.js, Web3.js, Metamask, Chainlink Oracles
• Patterns: tokens, events, verifying data integrity

• Java, C++, Golang, C#
• Algorithmic reps: LeetCode, HackerRank, Codewars
• Core CS: data structures, OOP, concurrency basics