Advanced Forward Engineering - Implementation Engineer - Senior

The opportunity

In EY’s Office of the CTO, the Advanced Forward Engineering (AFE) team is comprised of cells of Forward Deployed Engineers (FDEs) that are embedded with our strategic customers to ensure the best possible customer experience in using EY’s products.

We are seeking senior engineers for this team to design, build, and deliver AI‑native systems directly into highly regulated client environments such as tax, finance, and risk. This role combines end‑to‑end delivery ownership with hands‑on engineering execution, serving as the primary technical and outcomes authority for forward‑deployed client engagements.

Unlike traditional engineering or architecture roles, the AFE teams operate at the edge of the enterprise, embedding with clients, navigating real‑world constraints, and ensuring AI solutions are not only technically sound, but deployable, auditable, and trusted in production. This role is ideal for a seasoned engineer who excels at owning ambiguous problems, making pragmatic tradeoffs, and turning platform capabilities into real business impact.

AFE engineers are responsible for both shaping the solution and writing production‑quality code, while collaborating closely with platform, architecture, security, and governance partners.

Your key 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.

Skills and attributes for success

• Demonstrated ability to take ownership of complex, ambiguous problems and drive them to successful outcomes.
• Strong engineering fundamentals combined with pragmatic systems thinking in AI‑native environments.
• Experience in operating directly with clients and business stakeholders while remaining deeply technical.
• Capable of balancing hands‑on execution with shaping, explanation, and mentoring.
• Calm judgment under pressure; able to make defensible tradeoffs in regulated settings.
• Clear communicator who can translate technical details into business‑relevant outcomes.
• Bias toward reliability, repeatability, and trust over experimental novelty in production contexts.
• Curiosity and adaptability in rapidly evolving AI tooling and delivery practices.

To qualify you must have

• 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.

Ideally, you'll also have

• Bachelor’s or Master’s degree in Computer Science or related technical field.
• Experience in forward‑deployed engineering roles.
• Familiarity with AI safety, cost controls, or governance considerations in production systems.
• Exposure to platform integration, reference implementation development, or system hardening.
• Experience delivering solutions in regulated industries (financial services, tax, healthcare, risk).