Lead AI Agent Engineer & Architect

EY; City of Albany (NY); Hybrid; USD 126,000 - 230,000; Full time

Job summary

EY's Challenger Core seeks an accomplished software/ML engineer to build AI agents that execute long‑running workflows in enterprise settings. You will architect deterministic loops, tool integration, and robust governance to keep outcomes verifiable and safe.

You'll work at the intersection of applied AI, distributed systems, and product engineering, shipping real‑world agent‑driven flows, handling failures, and balancing autonomy with user control in a hybrid, travel‑friendly role.

Qualifications

• Bachelor’s Degree in a relevant field of study.
• 7-9 years of experience in software or applied machine‑learning engineering, with ownership of production systems and their live behavior.
• Hands‑on evidence of shipping large‑language‑model applications, agents, or complex model‑driven workflows used for 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 flexibility to go into the office in a hybrid capacity.

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 Core seeks an accomplished software/ML engineer to build AI agents that execute long‑running workflows in enterprise settings. You will architect deterministic loops, tool integration, and robust governance to keep outcomes verifiable and safe.

You'll work at the intersection of applied AI, distributed systems, and product engineering, shipping real‑world agent‑driven flows, handling failures, and balancing autonomy with user control in a hybrid, travel‑friendly role.