AI Product Engineer: End-to-End Builder

EY

Minneapolis (MN)

Hybrid

USD 107,000 - 201,000

Full time

9 hours ago

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Job summary

EY Challenger is a new program focused on creating the firm’s next generation of AI-enabled services. You’ll turn AI demonstrations into durable products, working close to users and production code in a fast, cross-functional team.

This role emphasizes founder-like ownership, rapid prototyping, and responsible AI use, with travel up to 50% and a hybrid office model across US locations. EY offers a broad compensation package and a path to shaping a better working world.

Qualifications

5+ years of relevant experience.
Bachelor’s Degree in a relevant field.
End-to-end delivery: A record of taking customer- or employee-facing software from an unclear concept through launch, learning, and adoption.
Technical breadth: Strong full-stack capability across modern frontend, backend, API, data, and cloud technologies.
Applied AI experience: Hands-on experience building products with large language models, retrieval, tools, or comparable AI application patterns.
Evaluation experience: Evidence of automated testing and behavioral evaluation for systems with variable or probabilistic outputs.
Live-service ownership: Telemetry, debugging, reliability, incident response, recovery, and performance improvement.
User-centered practice: Working directly with users, designers, and product leaders to turn evidence into better product decisions.
Engineering craft: Clear specifications, maintainable code, thoughtful review, and sound technical trade-offs.
Travel and On-site requirements: Up to 50% travel and hybrid office presence.

Responsibilities

Start with the user. Work directly with users and domain experts to understand real workflows, observe friction, and identify where AI can create distinctive value.
Test assumptions quickly. Turn product hypotheses into concise specifications, prototypes, and small releases that generate useful evidence early.
Own complete product slices. Build coherent verticals across the experience, services, data, AI behavior, and enterprise integrations rather than optimizing a single layer.
Shape AI behavior. Design instructions, context, retrieval, structured outputs, and tool use, choosing adaptation or tuning only when evidence supports it.
Build the evidence system. Create representative tasks and automated evaluations that show whether the product is useful, correct, safe, accessible, and improving.
Connect outcomes to behavior. Instrument user journeys so the team can relate adoption and task success to system behavior, latency, failures, and cost.
Engineer for responsible use. Build clear permission boundaries, secure data handling, human checkpoints, graceful degradation, recovery, and rollback into the experience.
Own production quality. Release incrementally, monitor live behavior, respond to incidents, and turn failures into durable product and engineering improvements.
Make practical trade-offs. Balance experience, quality, speed, reliability, and cost, using deterministic software when it is the more dependable choice.
Contribute reusable patterns. Share proven components and evaluation assets with the broader platform while remaining accountable for their performance in the product workflow.