A Full Stack Machine Learning Engineer dedicated to the complete lifecycle of artificial intelligence. Translating theoretical models into robust, scalable production systems. Specializing in bridging the gap between data science and DevOps by orchestrating end-to-end pipelines, enforcing rigorous testing standards, and ensuring system observability.

Core Philosophy

• Holistic Engineering: Managing the entire stack -> from data ingestion and model training to containerization and cloud deployment.
• Operational Excellence: Implementing industry-standard CI/CD, reproducible environments, and automated testing to eliminate "works on my machine" issues.
• System Reliability: Prioritizing monitoring and drift detection to maintain model performance in real-world scenarios.

Languages & Development Environment

Core AI, GenAI & Data Stack

MLOps, CI/CD & Containerization

Cloud Infrastructure & Monitoring

NexusAI TalentScout

• Overview: Autonomous recruitment agent replacing keyword matching with deep semantic understanding.
• Architecture: LangGraph agents powered by Neo4j knowledge graphs.
• Impact: Evaluates candidate potential by understanding skill relationships rather than just syntax.

Project Veda

• Overview: Local-first virtual human interface for secure, low-latency interaction.
• Architecture: Edge-deployed Computer Vision and Voice synthesis.
• Impact: Enables natural human-AI communication without reliance on external cloud dependencies.