Professional Overview
Applied AI Engineer with hands-on experience developing, evaluating, and optimizing LLM and RAG systems in production-oriented environments. Strong Python and ML background with experience in NLP systems, embeddings, vector retrieval, prompt engineering, and experimental evaluation of AI systems.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Contributed to production LLM and RAG systems across optimization workflows
- Performed embedding optimization through dataset preprocessing using Python
- Produced technical reports and dashboards to identify failure patterns
Key Achievements / KPIs
- Improved RAG accuracy by up to 15%
- Designed and maintained multilingual evaluation datasets (20–2000+ prompts)
- Built an internal modular RAG benchmarking pipeline
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Contributed to production LLM and RAG systems across optimization workflows
- Performed embedding optimization through dataset preprocessing using Python
- Produced technical reports and dashboards to identify failure patterns
Key Achievements / KPIs
- Improved RAG accuracy by up to 15%
- Designed and maintained multilingual evaluation datasets (20–2000+ prompts)
- Built an internal modular RAG benchmarking pipeline
Education
Field of Study: AI Engineering
Field of Study: Artificial Intelligence
Field of Study: Philosophy
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.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
