Senior AI Delivery Engineer – Client-Facing

Ernst & Young Oman

Columbia (SC)

Hybrid

USD 110,000 - 177,000

Full time

14 days+

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Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Paid time off

Hybrid work model

Job summary

EY is seeking senior engineers for the Advanced Forward Engineering (AFE) team, embedded with strategic clients to deliver AI-native systems in regulated environments. You will own end-to-end delivery, shaping solutions, writing production-grade code, and collaborating with platform, security, and governance teams to ensure auditable, trusted production deployments.

Ideal candidates bring 5+ years delivering complex software in enterprise or regulated settings, with hands-on experience in

Qualifications

5+ years of experience delivering complex software systems in enterprise or regulated environments.
Experience in customer-facing or consulting engineering roles.
Hands-on experience building and deploying distributed systems, APIs, integrations, or data-intensive applications.
Practical experience with modern AI application stacks (e.g., LLM-backed systems, agentic workflows, orchestration frameworks).
Proven track record owning delivery in real-world conditions.
Experience writing and maintaining production-grade code that operates under compliance, performance, and reliability constraints.
Ability to work effectively with engineering, architecture, security, and governance teams.

Responsibilities

Embed directly with client teams to frame problems, identify high-leverage opportunities, and define delivery paths under regulatory and operational constraints.
Own end-to-end delivery outcomes for engagements, from initial shaping through implementation, deployment, and early sustainment.
Implement and manage AI-enabled workflows, agentic systems, orchestration logic, and integrations using approved platform capabilities and delivery templates.
Write and review production-quality code (e.g., LangGraph, Python, orchestration glue, integration layers) aligned with enterprise delivery standards.
Make real-time delivery tradeoffs balancing functionality, safety, cost, and timeline, and clearly explain those decisions to stakeholders.
Ensure solutions comply with security, AI integrity, and FinOps constraints, working with control authorities to achieve safe-to-ship approval.
Capture and communicate field learnings, edge cases, and failure modes, contributing evidence-based feedback that improves platform evolution.
Collaborate with integration, platform, and architecture teams to reduce rework and enable repeatable delivery patterns.