Distinguished Engineer - AI Advanced Forward Engineering - Senior Manager

EY•Hartford, DC

12d•$144,000 - $374,000•Hybrid

About The Position

We are seeking a senior, hands-on Distinguished Engineer to lead the delivery of AI-native systems into highly regulated industries such as tax, finance, and risk. This role sits at the intersection of real-world delivery, architecture enforcement, and platform feedback, ensuring that AI systems move from concept to production with speed, safety, and audit-grade rigor. Unlike traditional architecture or product roles, the Advanced Forward Engineering (AFE) team is accountable for making AI systems work in reality, under client constraints, regulatory scrutiny, and operational pressure. Client engagements involve cells/pods of Forward Deployed Engineers and the Distinguished AFE operates as a cross‑cell technical leader, partnering closely with Product Engineering, Product Architecture, and governance authorities while remaining anchored in delivery outcomes. This role is ideal for a seasoned engineer who combines deep technical judgment with strong client presence, systems thinking, and the ability to translate messy field reality into durable, reusable patterns for the enterprise.

Requirements

10+ years of experience delivering complex AI enterprise systems, ideally in regulated industries.
Deep experience with distributed systems, cloud platforms, and integration‑heavy architectures.
Hands‑on experience with modern AI‑enabled application stacks (e.g., agentic systems, orchestration frameworks, LLM‑backed services).
Proven ability to operate in forward‑deployed or customer‑facing engineering roles, owning outcomes under real‑world constraints.
Track record of creating reusable technical patterns or platforms that scale beyond a single engagement.
Experience collaborating closely with architecture, product, and platform teams.
Strong understanding of software delivery lifecycle, quality gates, and production readiness in enterprise settings.
Deep technical expertise across a broad range of technology domains (AI/ML, Software Engineering, Systems Design, Security, Compute, Data, Networking, DevOps).
Strong product intuition with the ability to transform field observations into executive-ready recommendations, roadmap inputs, and reusable platform capabilities.
Ability to balance immediate delivery needs with long-term platform evolution, ensuring short-term solutions contribute to enduring enterprise capabilities.
Operate effectively across delivery, architecture, product, and governance domains simultaneously.
High credibility with senior engineers, architects, and client stakeholders.
Pragmatic mindset: balances ideal design with real constraints (regulatory, organizational, environmental).
Clear communicator who can explain complex technical tradeoffs with evidence and precision.
Bias toward repeatability and long-term maintainability over one-off solutions.
Intellectual curiosity and continuous learning orientation in AI-native development and system design.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science or related technical field.
Expertise with AI/ML lifecycle management, observability, and validation approaches.
Experience working with AI governance, security, and cost controls in production environments.
Ability to influence client and internal stakeholders, and platform direction through technical credibility and evidence.
Experience building reference implementations, test harnesses, and validation frameworks.
Exposure to regulated delivery contexts (e.g., financial services, tax, healthcare, risk platforms).

Responsibilities

Serve as a cross-engagement technical authority, ensuring delivery implementations adhere to architecture intent, safety, and economic constraints in regulated environments.
Synthesize learnings from multiple delivery cells into durable reference integrations, validated implementations, and reusable patterns that reduce repeated failure modes.
Work with AI agents to support regulated safe-to-ship decisions by contributing technical evidence and validation inputs.
Operate as a player-coach, contributing directly to complex system design and implementation while guiding delivery teams toward consistent, repeatable approaches.
Own the consolidation, prioritization, and communication of cross-cell product signals, converting field observations and client-specific adaptations into evidence-based recommendations, distinguishing them from isolated customer requests.
Partner with Product Engineering and Product Architecture to package consolidated client patterns into technical business cases, backlog candidates, and platform investment recommendations.
Participate in shaping sessions, architecture reviews, and prioritization discussions, advocating changes that improve portability, operability, and customer outcomes.