Applied AI / LLM Engineer
Building reliable RAG systems, tool-using agents, and evaluation pipelines.
I build production-minded AI systems with Python, LangGraph, LangChain, FastAPI, OpenAI, Anthropic, and local models. My work focuses on retrieval, agent reliability, evaluation, observability, and cost-aware execution.
I am a 2026 B.Tech graduate and AI Engineering Fellow at Maven (AI Makerspace), open to Applied AI / LLM Engineer opportunities with Indian and international AI teams.
- Built four end-to-end LLM applications across agents, RAG, local inference, and evaluation.
- Engineered DevMind with six security-aware tools, persistent sessions, runtime metrics, plugins, CI, and 156 tests.
- Built OpenAI AutoData with persistent budget controls, fail-closed validation, auditable outputs, and offline test coverage.
- Completed Andrew Ng's five-course Deep Learning Specialization and continue studying production RAG and AI evaluation.
- Based in India and open to remote, hybrid, or on-site Applied AI / LLM roles.
| Project | What it does | Engineering signals |
|---|---|---|
| DevMind | Terminal-native AI coding agent built with Python, LangGraph, and Claude | Six built-in tools, persistent sessions, runtime metrics, plugins, cross-platform support, 156 tests, CI |
| OpenAI AutoData | Generates hard research QA data through challenger, solver, and judge agents | Persistent budget guard, fail-closed validation, auditable outputs, offline tests, CI |
| TrustDI Agentic RAG | Agentic RAG system for trustworthy enterprise data integration and schema matching | Adaptive routing, evidence-backed decisions, OpenAI API explanations, FastAPI, Streamlit, CI |
| Corrective Agentic RAG Assistant | Adaptive CRAG assistant that detects retrieval failure and corrects noisy context | Query router, corrective retrieval actions, hierarchical retrieval, RAG metrics, CI |
| MemoryOS Agent | MemGPT-inspired long-term memory agent with OpenAI API support | Memory lifecycle controls, selective forgetting, SQLite store, Streamlit dashboard, CI |
| Adaptive RAG CAG Project | Adaptive RAG and Cache-Augmented Generation demo for retrieval workflows | Streamlit app, retrieval strategy comparison, local demos, test coverage |
- Agentic workflows with measurable quality gates
- RAG systems, retrieval quality, and grounded generation
- Tool-using agents with observability and cost controls
- AI evaluation, reliability, and security boundaries
- Python APIs and local-model integration
- Local RAG with Ollama and ChromaDB: private PDF question answering with local inference and embeddings.
- AI Reddit Brand Monitor: local sentiment, topic, urgency, and feedback analysis for Reddit mentions.
- LangGraph Ollama Chatbot: stateful local chat with SQLite checkpoints and token streaming.
- Agno Basics: practical examples covering tools, memory, RAG, teams, and agent workflows.
Python LangGraph LangChain FastAPI OpenAI API Anthropic API Ollama ChromaDB PyTorch Docker SQLite Streamlit
- Build the smallest reliable system that proves the idea.
- Test failure paths, not only happy paths.
- Make cost, state, and model behavior visible.
- Keep claims aligned with reproducible code and results.
I am open to Applied AI / LLM roles and focused open-source collaboration. If you find a project useful, follow the profile for upcoming builds or star the repository you want to revisit. Technical feedback is always welcome.
Based in India. The strongest repositories are pinned below for a quick technical review.