I'm an AI Engineer focused on MLOps & LLMOps, building production-oriented AI systems, RAG pipelines, multi-agent workflows, and end-to-end ML applications.

With a healthcare background and hands-on experience in machine learning, deep learning, observability, and AI infrastructure, I enjoy turning complex problems into reliable, practical, and scalable AI solutions.

Currently, I’m focused on LLM applications, Agentic AI, RAG systems, AI observability, FastAPI services, and MLOps workflows.

• 🧭   Building CairnOps — an agentic expedition planning platform with LangGraph, RAG, Qdrant and safety-aware reasoning
• 🤖   Developing LinguaGate — an AI Ops prototype for HR lead enrichment, scoring, routing and outreach automation
• 🛰️   Building OrbitDecay MLOps Platform with FastAPI, Jenkins, MLflow, Prometheus, Grafana and PostgreSQL
• 🎵   Created Therapy Tunes — an emotion-aware music recommender using ML models and Streamlit
• 🧠   Working on multi-agent workflows, RAG evaluation, AI observability and production-ready LLM systems
• 💬   Ask me about Python, FastAPI, LangGraph, LangChain, RAG, MLOps, LLMOps and AI systems
• 🌟   Fun facts: Dog lover 🐕 | Calisthenics enthusiast 🤸🏽 | Weekend hiker ⛰️ | Coffee addict ☕

LangGraph-based multi-agent AI system for route analysis, weather forecasting, equipment planning, risk assessment and itinerary generation.

Tech: Python, LangGraph, LangChain, Ollama, Qdrant, Langfuse, Ragas, FastAPI, Docker

AI Ops prototype for HR lead enrichment, scoring, routing, outreach generation and CRM-style tracking.

Tech: Python, FastAPI, LangGraph, LangChain, Ollama, Pydantic, PostgreSQL, MLflow, Langfuse, Streamlit, Docker

Satellite decay risk prediction system with microservice-based architecture, ML pipelines, monitoring and CI/CD workflows.

Tech: Python, Rust, Polars, TensorFlow, FastAPI, Docker, Jenkins, MLflow, Prometheus, Grafana, PostgreSQL, MinIO

Mood-based music recommendation system using machine learning models, clustering, sentiment analysis and an interactive Streamlit dashboard.

Tech: Python, Pandas, Scikit-learn, BeautifulSoup, Streamlit

• Production-ready LLM applications
• RAG pipelines and retrieval evaluation
• Multi-agent AI systems with LangGraph
• MLOps / LLMOps observability
• FastAPI-based AI services
• Model monitoring, tracing and evaluation workflows