AI prompt engineer and AI engineer with extensive experience in quality assurance methodologies, systematic evaluation frameworks, and performance optimization for AI training initiatives. Combines deep technical expertise in AI model development with proven ability to design and implement comprehensive QA processes, including manual sampling, structured benchmark testing, and quantitative assessment workflows. Proficient in Python programming and SQL query analysis with hands-on experience in API-driven testing platforms and debugging infrastructure implementation.
Quality Assurance & Process Development
Programming & Development
Data Analysis & Validation
AI/ML Training & Evaluation
Cross-functional Collaboration & Communication
Design Prompts for Everyday Work Tasks | Google | Aug 2025
Introduction to LLM | Sololearn | Jul 2025
Prompt Engineering | Sololearn | Jul 2025
Python Intermediate | Sololearn | Jul 2025
Ask Questions to Make Data-Driven Decisions | Coursera | Jun 2022
Introduction to SQL | DataCamp | Jun 2022
Technical Portfolio
Documented prompt engineering methodologies, AI system design, and evaluation frameworks with quantitative improvement metrics. Includes a Mimir case study featuring quality assessments, problem/solution documentation, and measurable results from systematic optimization approaches.,
Mimir | AI Chatbot
Interactive educational AI system demonstrating LangChain/LangGraph orchestration, dynamic conversation management, and advanced prompt engineering in a production environment.