Lead AI Agent Engineering Manager

EY; Austin (TX); Híbrido; USD 126 000 - 262 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Medical and dental coverage

Pension and 401(k) plans

Flexible vacation policy

Paid time off

Resumo da oferta

EY is hiring for an AI/Applied ML engineer to build and operate durable, safe, and observable agent-based systems across enterprise integrations. The role emphasizes deterministic workflows, secure execution, and clear human interventions, with up to 50% travel and hybrid office attendance.

Ideal candidates bring 7–9 years of software or ML engineering experience, proficiency in Python/TypeScript, and a track record shipping model-driven workflows in production.

Qualificações

• Bachelor’s Degree in a relevant field of study.
• 8+ years of professional software or applied ML engineering experience with ownership of production systems.
• Hands-on evidence of shipping large-language-model applications, agents, or complex model-driven workflows used in real work.
• Strong capability in Python, TypeScript, or another language used for production services and integrations.
• Experience with model APIs, structured outputs, retrieval, tool use, and evaluation of variable behavior.
• Practical command of asynchronous work, state, concurrency, idempotency, retries, and failure recovery.
• Experience implementing access boundaries and scoped authority for applications or automated workloads.
• Evidence of improving live systems through traces, metrics, logs, incident analysis, and durable remediation.
• This role requires up to 50% travel and hybrid office attendance.

Responsabilidades

• 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.

Conhecimentos

Production engineering

Agent architecture

AI application depth

Evaluation discipline

Security and operability

Product judgment

Formação académica

Bachelor’s Degree in a relevant field

Ferramentas

Python

TypeScript

Descrição da oferta de emprego

EY is hiring for an AI/Applied ML engineer to build and operate durable, safe, and observable agent-based systems across enterprise integrations. The role emphasizes deterministic workflows, secure execution, and clear human interventions, with up to 50% travel and hybrid office attendance.

Ideal candidates bring 7–9 years of software or ML engineering experience, proficiency in Python/TypeScript, and a track record shipping model-driven workflows in production.