Professional Overview
AI/ML professional specializing in LLM evaluation, prompt engineering, RAG, AI agents, and model quality. Experienced in building and testing generative AI applications, improving outputs through human feedback, and using Python to support data preparation, validation, and workflow automation.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Developed domain-specific prompts for LLM evaluation across coding and STEM task
- Evaluated AI responses for factual accuracy, relevance, clarity, and reasoning
- Rewrote model outputs to correct errors and improve technical explanations.
- Reviewed complex codebases and created prompts to test coding capabilities.
- Provided actionable feedback to improve model accuracy and response quality.
Key Achievements / KPIs
- Improved the quality of AI-generated coding and STEM responses.
- Identified technical errors, unclear reasoning, and weaknesses in model outputs.
- Supported LLM research through structured prompt creation and evaluation.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built RAG-powered document assistants for enterprise knowledge retrieval.
- Developed LLM customer-support chatbots to automate user inquiries.
- Created AI agents for email drafting, scheduling, and workflow planning.
- Prepared and validated data throughout the Gen AI development lifecycle.
- Tested models using statistical, technical, and business performance criteria.
Key Achievements / KPIs
- Delivered production-focused RAG assistants and LLM chatbot solutions.
- Automated routine internal workflows through task-oriented AI agents.
- Improved model reliability through structured testing and data validation.
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.
