// ML Engineer · GenAI Developer · AI Engineer
I build end-to-end ML systems and LLM-powered applications — RAG pipelines, agentic workflows, reasoning interfaces. From LangGraph orchestration to production FastAPI deployments, I close the gap between research and real users.
// The Problem I Solve
LLMs hallucinate. RAG fixes that — if you build it right.
Generic LLM calls break in production. I engineer retrieval pipelines with FAISS, ChromaDB, and proper chunking strategies so answers are strictly grounded in your data.
Agents that work in notebooks fail in the real world.
I build LangGraph-orchestrated agents with state machines, intent classifiers, and fallback paths — designed to handle edge cases before your users find them first.
Great models stuck behind bad infra ship nothing.
FastAPI backends, Docker containers, AWS EC2 deployments, Redis caching — I take ML from Jupyter notebooks to production-grade APIs that actually scale.
// Who I Am
I'm Adarsh Pandey and I have deep hands-on expertise across the entire AI stack — from neural network fundamentals to deploying multi-agent systems in production. Certified by Anthropic, Google, and Kaggle. Obsessed with building things that actually work.
01 — GenAI & LLMs
LangChain, LangGraph, LangSmith — the full agentic stack. RAG pipelines with FAISS & ChromaDB. Groq API, Gemini API, Ollama for local deployment. Prompt engineering that gets results.
// Generative AI Stack
Orchestration
LangChain LCEL LangGraph LangSmithVector Stores
FAISS ChromaDB HuggingFace EmbeddingsLLM APIs
Groq API Gemini API OllamaPatterns
RAG Pipelines Prompt Engineering Agents & Memory02 — ML & Deep Learning
Supervised & unsupervised learning, XGBoost, Random Forest, CNNs, RNNs, LSTMs, Transformers, HuggingFace. Feature engineering, cross-validation, evaluation metrics — the full pipeline.
// ML & Deep Learning Stack
Frameworks
PyTorch TensorFlow HuggingFace Scikit-learnModels
XGBoost Random Forest LSTM / GRU TransformersData
NumPy Pandas Matplotlib Power BI03 — Backend & DevOps
FastAPI, Node.js, Express.js, REST APIs, WebSockets, MongoDB, MySQL, Redis, Docker, AWS EC2, Nginx. Full production-grade backend engineering.
// Backend & Infrastructure
APIs & Servers
FastAPI Node.js Express.js StreamlitDatabases
MongoDB MySQL Redis SQLiteDevOps
Docker AWS EC2 Nginx Git / GitHub// Projects
LangGraph-orchestrated AI agent with per-thread PDF RAG (FAISS + all-MiniLM-L6-v2), DuckDuckGo web search, Alpha Vantage stock lookup, and calculator — dynamically routed by Llama 3.3 70B via Groq. Persistent multi-thread memory via SqliteSaver, auto-generated chat titles, and full conversation restore.
Production-style LangGraph sales agent with a two-node directed graph (detect_intent → generate_response) managing persistent TypedDict state across 5+ conversation turns. LLM-powered 3-class intent classifier decoupled from response generation, with state-machine-enforced lead capture that prevents premature execution by design.
Full RAG pipeline using LangChain LCEL for semantic Q&A over any YouTube video's transcript. FAISS vector store with all-MiniLM-L6-v2 embeddings (chunk_size=1000, overlap=200), top-k retrieval, multilingual transcript handling, and conversational memory via last-3-exchange injection into every prompt.
Multi-turn conversational AI interface powered by DeepSeek-R1-0528 reasoning LLM via LangChain and HuggingFace Inference API. Regex-based <think> block parsing to separate model reasoning from final answers, with a toggleable reasoning panel. Persistent multi-turn memory and optimized loading via @st.cache_resource.
// Early Numbers
end-to-end LLM-powered applications built and deployed
AI/ML frameworks mastered across the full stack
industry certifications from Anthropic, Google, Kaggle, and HP
// Experience
// How I Work
Architecture Discovered
Deployment Status
// Certifications
Anthropic
Issued March 2026 · AI agent workflows, MCP architecture, AI-assisted coding
Kaggle × Google
Issued December 2025 · AI Agents, Generative AI, LLMs, Prompt Engineering
Google for Education
Issued November 2025 · Valid November 2028 · Google Workspace, Data-Driven Collaboration
HP LIFE (HP Foundation)
Issued 2025 · AI for Beginners, Data Science & Analytics, AI for Business, Agile
// Achievements
2x champion of coding competition consecutively, competed against multiple competitors and we all were restricted to basic technologies for implementations. Restricted to plan, acquire perfect resources from given pool of resources and implementation of our thoughts and then testing whether it works as expected and then to present infront of expert within only 90 minutes.
Kaggle × Google
Completed all 5 days of the intensive program co-hosted by Kaggle and Google, earning the official badge. Covered agentic AI architectures, tool use, and multi-agent systems — directly aligned with my ongoing LangGraph and MCP work.
Secured 1st position in the university-level presentation competition, demonstrating skills in structured storytelling, visual communication, and technical clarity.
Open to ML Engineer, GenAI Developer, and full-stack AI roles.
Also open to interesting freelance projects and collaborations.