Professional Overview
I'm an applied AI engineer and data scientist who builds machine learning and LLM-powered solutions for real-world problems. I hold a B.Sc. in Biochemistry and certifications in AI, machine learning, and advanced data analytics (IBM, Google, NITDA/3MTT), so I'm comfortable working with scientific and technical data and turning it into working systems. What I do Build retrieval-augmented generation (RAG) pipelines: document ingestion, chunking, embeddings, vector search, and grounded LLM responses Build and deploy predictive models with machine learning and deep learning Prepare data end to end: cleaning, transformation, feature engineering, and validation Evaluate models and RAG systems with clear metrics, such as retrieval quality, answer accuracy, and hallucination checks Communicate results through dashboards and visualizations in Tableau and Python Stack: Python, Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, SQL, Matplotlib, Seaborn, Tableau. For RAG: LLM APIs, embeddings, and vector databases.
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.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
