Professional Overview
Full-Stack AI Engineer | GenAI, MCP, RAG, .NET, MERN, Langchain, LlamaIndex, PyTorch | I architect advanced RAG pipelines, mission-critical Multi-Agent systems, and scalable full-stack applications. Expert in optimizing LLMs and connecting them to data & tools via Model Context Protocol. Delivering high-performance, intelligent software solutions.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Full-Stack & Instrument Software R&D
- Advanced GenAI & RAG Integration
- Next-Gen Tool Connectivity (MCP)
- System Performance & Optimization
- Cross-Functional Collaboration & Agile Delivery
Key Achievements / KPIs
- RAG-based GenAI agent reducing engineering information-retrieval time by 40%
- shipped over 40 critical pull requests for core instrument projects
- MCP for multi-agent workflows, achieving a 25% reduction in API/LLM latency
- improving system processing throughput by 15% for high-volume testing data
Education
Field of Study: Machine Learning, Statistics, Computer Vision
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
