Andrew Fares

About

Built production AI systems inside AWS finance/procurement, including an LLM-based supplier quote classification and price-vetting workflow that drove $5M in savings over 3 months. Combines GenAI evaluation expertise, internal platform design, and reusable Python data-quality tooling with strong cross-functional execution across finance, accounting, and hardware engineering.

Key Strengths

• end-to-end ownership of production AI systems
• LLM-powered classification and RAG evaluation design
• human-in-the-loop AI quality improvement
• cross-functional collaboration with finance, accounting, and hardware engineering
• measurable business impact: $5M savings in 3 months
• strong AI evaluation and safety practices
• platform thinking for reusable internal tooling
• cost/latency/scalability tradeoff analysis
• Python library design for internal engineering teams
• data quality and reliability engineering

Reference Highlights

• exceptional end-to-end ownership
• built systems from the ground up
• raises the bar on production readiness
• strong security mindset
• improves logging and observability
• owns systems end-to-end
• builds scalable production pipelines
• automates previously manual processes
• reduces operational burden
• enables organizational scale without proportional headcount growth
• strong LLM evaluation expertise
• applies industry research effectively
• high-quality engineering fundamentals
• excellent engineer
• high code quality
• strong design caliber
• identifies bottlenecks effectively
• proposes alternatives with pros and cons
• optimizes for cost and latency
• exemplifies best of AWS engineering

Experience

Education

Awards

• Cum Laude - Maryville University of Saint Louis

Skills

Languages