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The opportunity

Turn emerging AI capabilities into products people choose to use, trust, and rely on. You will work as an end-to-end builder, close to users, product decisions, and production code, connecting the pieces that turn an AI demonstration into a durable product.

This role suits an engineer with founder-like ownership and the discipline to test assumptions early. You may move from a user conversation to a working prototype, from that prototype to a secure service, and from a production failure to a better evaluation in the same product cycle. Speed matters, but so do accessibility, maintainability, and the willingness to simplify when the evidence calls for it.

About Challenger

Challenger is a new EY program focused on building the firm's next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones.

Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale.

Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build.

Your key 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.

Skills and attributes for success

+ Full-stack engineering: Strong ability to build maintainable production software across modern interfaces, services, APIs, data, and integrations.

+ AI application depth: Practical knowledge of model APIs, context design, retrieval, tool use, structured outputs, and agentic patterns.

+ Product judgment: Ability to find the real user problem, frame clear hypotheses, and simplify the solution when evidence challenges the original idea.

+ Evaluation discipline: Experience designing representative tests and experiments for systems whose behavior is not fully deterministic.

+ Production ownership: Strong instincts for observability, accessibility, security, performance, reliability, recovery, and responsible use.

+ High-agency collaboration: Comfort working across product, design, domain, platform, and control teams while staying accountable for the outcome after launch.

To qualify you must have

+ 7+ years of relevant and applicable experience

+ Bachelor's Degree in a relevant field of study

+ 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, demonstrated through shipped work.

+ 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: Demonstrated responsibility for telemetry, debugging, reliability, incident response, recovery, and performance improvement.

+ User-centered practice: Experience working directly with users, designers, and product leaders to turn evidence into better product decisions.

+ Engineering craft: A consistent record of clear specifications, maintainable code, thoughtful review, and sound technical trade-offs.

+ This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.

Ideally, you'll also have

+ Agentic products: Experience building workflow automation or collaborative AI products that interact with real business systems.

+ Enterprise environments: Experience with identity, knowledge systems, permission-sensitive information, or regulated and confidential settings.

+ Design sensibility: A strong instinct for clear interactions, accessibility, and reducing user effort in complex workflows.

+ Efficiency at scale: Experience improving the quality, latency, reliability, or economics of production AI applications.

+ Builder track record: Startup experience, open-source contributions, or another clear example of high-agency product building.

We welcome exceptional candidates from product, service design, operations, transformation, policy, domain, and entrepreneurial backgrounds; no particular degree or certification is required.

What we offer you

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