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We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

The opportunity

Build AI agents that carry meaningful work from intent to a verified outcome, not merely produce an impressive response. You will engineer the execution loops, tools, context, permissions, evaluations, and runtime controls that let agents operate across complex workflows while remaining understandable, steerable, and safe when systems or assumptions fail.

This role sits where applied AI, distributed systems, and product engineering meet. You will work on long-running behavior, real enterprise integrations, and difficult production failures, deciding when a deterministic workflow is enough, when autonomy adds value, and whether each added degree of freedom earns its operational and user-experience cost.

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

+ Choose the right execution model. Design agent architectures that use deterministic steps, a single-agent loop, or coordinated agents only when measured performance justifies the complexity.

+ Build dependable tools. Create connectors and interfaces with clear contracts, validated outcomes, narrow authorization, and errors that help the agent recover or escalate.

+ Engineer agent behavior. Shape planning, routing, delegation, stopping, retry, and handoff behavior across complete workflows.

+ Create durable context. Combine instructions, user state, retrieval, memory, and environment feedback while preserving relevance, provenance, freshness, and permitted access.

+ Bound agent authority. Implement scoped identities, credentials, budgets, approval points, and emergency containment so agents act within explicit limits.

+ Evaluate complete trajectories. Build representative tests that measure verified task success, tool use, recovery, human takeover, and prohibited actions rather than isolated responses.

+ Operate long-running execution. Use durable state, checkpointing, safe retries, versioned releases, rollback, and graceful degradation to keep work recoverable.

+ Make behavior observable. Instrument end-to-end traces and replay so teams can understand failures, diagnose patterns, and turn production evidence into improvements.

+ Protect user control. Design clear intervention points so people can inspect, interrupt, redirect, and safely resume agent work.

+ Build shared capability. Partner across product, platform, knowledge, security, and evaluation teams and publish reusable agent patterns and operational lessons.

Skills and attributes for success

+ Production engineering: Strong software engineering and distributed-systems judgment, with practical experience building services that remain dependable under failure.

+ Agent architecture: Ability to reason about planning, tools, state, handoffs, and coordination while choosing a simpler workflow when it performs better.

+ AI application depth: Practical knowledge of model APIs, structured outputs, context, retrieval, memory, experimentation, and tool use.

+ Evaluation discipline: Experience measuring complete trajectories and verified outcomes for systems whose behavior is variable.

+ Security and operability: Strong instincts for least privilege, tool boundaries, observability, resilience, recovery, and accountable human intervention.

+ Product judgment: Ability to balance quality, latency, cost, and user control and explain technical trade-offs clearly across disciplines.

To qualify you must have

+ Bachelor's Degree in a relevant field of study.

+ 9-12+ years of relevant experience.

+ Relevant experience: A strong record in software or applied machine-learning engineering, with ownership of production systems and their live behavior.

+ Agent delivery: Hands-on evidence of shipping large-language-model applications, agents, or complex model-driven workflows used for real work.

+ Programming depth: Strong capability in Python, TypeScript, or another language used for production services and integrations.

+ Applied AI patterns: Experience with model APIs, structured outputs, retrieval, tool use, and evaluation of variable behavior.

+ Distributed execution: Practical command of asynchronous work, state, concurrency, idempotency, retries, and failure recovery.

+ Identity and controls: Experience implementing access boundaries and scoped authority for applications or automated workloads.

+ Production learning: Evidence of improving live systems through traces, metrics, logs, incident analysis, and durable remediation.

+ Travel and On-site requirements: This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.

Ideally, you'll also have

+ Long-running agents: Experience with durable agent execution, multi-agent systems, or human-agent collaboration.

+ Enterprise tools: Experience integrating agents with permission-sensitive business systems, browsers, or computer-use environments.

+ Interoperability: Experience with MCP or comparable standards, enterprise retrieval, knowledge graphs, or context services.

+ High-assurance automation: Experience with sandboxed execution, durable workflow engines, or systems requiring strong evidence and control.

+ Ecosystem contribution: Practical contributions to agent, evaluation, developer tooling, applied AI, or open-source communities

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

At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.

+ We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job is:

+ New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices - $172,800 to $359,000

+ Bay Area California offices - $180,000 to $374,000

+ All other offices l...

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