Senior AI Systems Architect & Delivery Lead

Ernst & Young Oman; Charleston (WV); Hybrid; USD 144,000 - 329,000; Full time

Benefits offered by this job

Medical insurance

Dental coverage

Pension

401(k)

Job summary

EY seeks a senior, hands-on Distinguished Engineer to lead AI-native delivery in regulated industries. You will drive architecture-enforced, production-grade AI systems with strong client presence and delivery outcomes.

You’ll partner with Product Engineering and governance teams to convert field observations into reusable patterns, while mentoring delivery squads and balancing risk, operability, and performance across complex enterprise environments.

Qualifications

• 10+ years of experience delivering complex AI enterprise systems.
• Deep experience with distributed systems, cloud platforms, and integration-heavy architectures.
• Hands-on experience with modern AI-enabled stacks and agentic systems.

Responsibilities

• Serve as cross-engagement technical authority ensuring delivery adheres to architecture, safety, and constraints.
• Synthesize learnings into durable reference integrations and reusable patterns.
• Work with AI agents to support regulated safe-to-ship decisions with evidence inputs.
• Operate as a player-coach contributing to complex system design and guiding delivery teams.
• Consolidate cross-cell signals into evidence-based recommendations for platform investments.
• Partner with Product Engineering and Architecture to package patterns into business cases and backlog.

Skills

AI/ML

Distributed systems

Cloud platforms

Security

System design

DevOps

Education

Bachelor’s or Master’s degree in Computer Science

Tools

Orchestration frameworks

LLM-backed services

Job description

EY seeks a senior, hands-on Distinguished Engineer to lead AI-native delivery in regulated industries. You will drive architecture-enforced, production-grade AI systems with strong client presence and delivery outcomes.

You’ll partner with Product Engineering and governance teams to convert field observations into reusable patterns, while mentoring delivery squads and balancing risk, operability, and performance across complex enterprise environments.