Senior AI Agent Engineer for Enterprise Systems

EY; Salt Lake City (UT); Hybrid; USD 144,000 - 374,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Flexible vacation policy

Job summary

EY's Challenger program seeks engineers to build AI agents spanning deterministic and autonomous workflows, with robust tool integration, context management, and safe execution across long-running processes.

The role sits at the intersection of applied AI, distributed systems, and product engineering, tackling enterprise-scale integrations, production failures, and nuanced cost/benefit decisions in automation.

Qualifications

• 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.

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 elevate.
• 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

Production engineering

Agent architecture

AI application depth

Evaluation discipline

Security and operability

Product judgment

Education

Bachelor’s Degree in a relevant field

Tools

Python

TypeScript

Job description

EY's Challenger program seeks engineers to build AI agents spanning deterministic and autonomous workflows, with robust tool integration, context management, and safe execution across long-running processes.

The role sits at the intersection of applied AI, distributed systems, and product engineering, tackling enterprise-scale integrations, production failures, and nuanced cost/benefit decisions in automation.