Professional Overview
Machine Learning Engineer specializing in Natural Language Processing (NLP), Large Language Models (LLMs), and Agentic AI architectures. Proven track record of engineering cutting-edge generative systems, automating scalable data ingestion pipelines, and deploying high-performance production models to cloud infrastructure (AWS/GCP).
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Engineered a UNet on QGIS 8-band geo-spatial data for coastline extraction.
- Drove a 43% reduction in RMSE (15.44 to 8.78).
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Engineered a UNet on QGIS 8-band geo-spatial data for coastline extraction.
- Drove a 43% reduction in RMSE (15.44 to 8.78).
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Optimized an agentic RAG pipeline (FastAPI, vLLM, Docker).
- Achieved a 4x latency reduction (∼20s to∼5s).
- Developed a hierarchical chunking and parsing approach for document processing.
- Boosted the retrieval recall@k scores by∼18% and reranker MRR@k scores by∼19%.
Education
Field of Study: Computer Science
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.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
