Applied AI Operations & Product Associate

Applied AI Operations & Product Associate 

Role Summary 

Own the evaluation and day-to-day performance of AI-driven workflows. This role focuses on interpreting AI outputs, validating whether they are correct in a real business context, and improving them through hands-on technical work. 

You will act as the bridge between AI systems and business outcomes—ensuring that what the AI suggests is not just technically plausible, but actually makes sense and delivers value. 

Core Responsibilities 

Work directly with AI systems (LLMs, workflows, internal tools) in real use cases 

Interpret AI-generated outputs, recommendations, and trade-offs 

Evaluate whether outputs are: 

logically sound 

contextually accurate 

aligned with business goals 

Identify when AI outputs are misleading, incomplete, or incorrect 

Diagnose root causes across: 

prompt design 

data/input quality 

workflow / logic structure 

Implement improvements through: 

prompt iteration 

workflow adjustments 

coding (Python / APIs) 

Test and validate outputs to ensure consistency and reliability 

Translate business needs into functional AI workflows 

Prioritize fixes and improvements based on business impact, not just technical correctness 

Partner with product / engineering to improve system behavior 

Must-Have Requirements 

Moderate coding ability (Python or similar) 

Able to write, read, and debug scripts independently 

Comfortable working with APIs, data structures (e.g., JSON), and basic workflows 

Strong problem-solving ability (can break down why something is not working or not making sense) 

Hands-on experience with AI tools beyond casual use 

Ability to move from problem → solution (not just analysis) 

Comfortable challenging outputs and identifying when something is “off” 

Ability to implement or modify scripts/workflows to test and improve AI behavior 

Business & Decision Judgment (Required) 

Ability to critically evaluate AI-generated outputs and recommendations 

Can assess whether suggested actions or trade-offs are actually valid in real-world scenarios 

Understands how outputs translate into business impact (e.g., user behavior, efficiency, cost, outcomes) 

Comfortable questioning assumptions rather than accepting outputs at face value 

Can distinguish between: 

technically plausible 

vs actually correct and useful 

Able to make judgment calls on what to act on vs what to ignore 

Nice to Have 

Experience building small AI or automation projects 

Familiarity with APIs or system integrations 

Experience working on products/tools used by real users 

Exposure to analytics, experimentation, or decision-making based on data 

What This Role Is NOT 

Not a pure PM / PMM role 

Not a data engineering or ML training role 

Not operations or coordination-focused 

Not responsible for building models from scratch 

Success Criteria 

Accurately identifies when AI outputs are incorrect, misleading, or low-value 

Makes sound judgments on whether AI recommendations should be trusted or challenged 

Implements improvements that increase real-world usefulness and reliability 

Consistently prioritizes work based on business impact 

Bridges the gap between AI capability and practical application