Senior AI Agent Architect — Enterprise Automation Leader

EY; Alpharetta (GA); Remote; USD 144,000 - 359,000; Full time

Benefits offered by this job

Flexible vacation policy

Medical and dental coverage

Pension and 401(k) plans

Job summary

EY is seeking a senior Applied AI Engineer to build autonomous agents that execute real work across complex workflows. You will design deterministic and multi-agent architectures, integrate tools, manage context and permissions, and ensure safety and observability in production systems.

This role blends AI, distributed systems, and product engineering to tackle long-running behavior, enterprise integrations, and difficult production failures.

Qualifications

• Bachelor’s degree required in a relevant field.
• 9–12+ years of relevant experience in software or applied machine-learning engineering with production systems.
• Hands-on experience shipping large-language-model applications, agents, or complex model-driven workflows.
• Programming depth in Python, TypeScript, or other production service languages.

Responsibilities

• Choose the right execution model and design agent architectures with deterministic steps or single-agent loops when justified by performance.
• Build dependable tools with clear contracts, validated outcomes, and safe error handling.
• Engineer agent behavior including planning, routing, delegation, and retry logic across workflows.
• Create durable context by integrating instructions, user state, memory, and environment feedback while preserving provenance and access control.
• Bound agent authority with scoped identities and budget controls to enforce explicit limits.

Skills

Distributed systems

Agent architecture

AI application depth

Security and operability

Product judgment

Education

Bachelor’s Degree in a relevant field

Tools

Python

TypeScript

Job description

EY is seeking a senior Applied AI Engineer to build autonomous agents that execute real work across complex workflows. You will design deterministic and multi-agent architectures, integrate tools, manage context and permissions, and ensure safety and observability in production systems.

This role blends AI, distributed systems, and product engineering to tackle long-running behavior, enterprise integrations, and difficult production failures.