Academy / Track 02
Junior AI Evaluation Engineer.
Building the workforce that creates trust in artificial intelligence. Building AI systems has become easier. Trusting them has not.
The opportunity
The future of AI needs more than builders.
Tools like Claude Code, OpenAI Codex, ChatGPT, Gemini, and autonomous agent frameworks have made it dramatically easier to build AI applications. As a result, companies are adopting AI rapidly — and asking harder questions.
How do we know the AI is giving accurate answers?
How do we know it is following company policies?
How do we know it is protecting customer information?
How do we know a recent change did not reduce quality?
How do we know an AI agent is completing work correctly?
How do we know an AI system is delivering business value?
These questions have created one of the fastest-growing needs in the AI workforce. Over the next three to five years, demand for people who can verify and govern AI is likely to outpace the supply of trained talent — with far fewer competitors in the space today.
The role
What is an AI Evaluation Engineer?
The quality-assurance professional for artificial intelligence. While developers focus on building systems, AI Evaluation Engineers answer a different question — can this system be trusted? They test applications, evaluate outputs, measure quality, detect risks, monitor production systems, and support compliance and governance.
Traditional software is predictable: 2 + 2 is always 4. AI is different — many possible responses, some excellent, some wrong. The work is determining which is which.
Modern AI applications combine language models, retrieval systems, knowledge bases, external tools, workflow automations, and autonomous agents. Powerful — and capable of failing in unexpected ways. Organizations need people who can find those failures before they reach customers.
What you’ll learn
The practical skills organizations need today.
Success depends more on analytical thinking, attention to detail, and curiosity than on advanced mathematics or programming.
Enterprise AI fundamentals
How modern AI systems operate — large language models, AI agents, retrieval-augmented generation, autonomous workflows, and multi-agent systems.
AI evaluation methodology
Industry best practices — evaluation frameworks, rubrics and scorecards, benchmark design, human evaluation, and automated evaluation.
Testing and measurement
How organizations measure performance — accuracy, relevance, faithfulness, groundedness, reliability, latency, and cost.
Evaluation platforms and tools
Exposure to leading technologies — OpenAI Evals, DeepEval, Ragas, LangSmith, Langfuse, Azure AI Foundry, and AWS Bedrock evaluation. When and why to use a tool, not memorizing one platform.
Governance and responsible AI
Enterprise practices for privacy, security, compliance, responsible AI, risk management, and documentation.
Program structure
12 weeks.
Instructor-led training, hands-on labs, project work, and professional development.
Weeks 1–2
Enterprise AI foundations
Weeks 3–5
AI evaluation principles and methodologies
Weeks 6–8
Industry evaluation tools and platforms
Weeks 9–10
Governance, compliance, and AI operations
Weeks 11–12
Capstone evaluation project
Career outcomes
Where graduates go.
AI Evaluation Engineer
AI Quality Analyst
AI Testing Specialist
AI Governance Analyst
Responsible AI Analyst
AI Operations Analyst
AI Reliability Analyst
AI Quality Assurance Specialist
As organizations deploy AI at scale, professionals who can measure and validate performance will become increasingly valuable.
Apply
Join the next generation of AI professionals.
Tuition is $3,000 for the full 12-week program. Cohorts are intentionally small, with mentorship and scholarships for qualified applicants. Secure your seat now, or reach out with any questions.