Forward Deployed Engineer - Applied AI - Senior Manager - Financial Services - Consulting

About the role

The opportunity involves leading the definition and delivery of AI system design principles, reference architectures, and engineering standards for highly complex AI/ML initiatives across the organization. It requires hands-on engineering experience and the ability to shape long-term technology direction.

Responsibilities

• Define and govern system design principles, reference architectures, and engineering patterns for AI/ML, generative AI, RAG, and agentic systems.
• Lead the most complex and escalated technical challenges across multiple teams, providing hands-on guidance in architecture, coding, troubleshooting, and design remediation.
• Own end-to-end architecture for strategic AI initiatives, including service boundaries, orchestration models, data contracts, evaluation frameworks, and operational guardrails.
• Drive consistency in engineering standards, design reviews, architecture governance, observability, resilience, security, and responsible AI practices.
• Shape the enterprise integration model for AI/ML components within broader product, platform, infrastructure, and client delivery ecosystems.
• Define and evolve API and integration strategies for AI platforms and applications, including contract design, versioning, security, idempotency, and reliability patterns at enterprise scale.
• Ensure API layers and application integration patterns decouple clients from internal AI service topology, enabling safe evolution of models, workflows, and data stores without breaking consumers.
• Lead large, complex project or program delivery outcomes by aligning architecture decisions, engineering execution, stakeholder governance, risks, dependencies, and delivery quality.
• Influence platform strategy, technical roadmaps, and investment decisions through deep engineering judgment and practical delivery insight.
• Partner with senior leaders across Engineering, Architecture, Product, Data, Security, Operations, and engagement leadership to align strategy with execution.
• Establish scalable approaches for model evaluation, benchmarking, experimentation, rollout controls, and production quality measurement.
• Mentor senior engineers and technical leads, raising the organization's bar for system design, technical depth, delivery rigor, and architectural decision-making.
• Identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities across the enterprise.

Requirements

The role demands a strong foundation in gen AI, including foundational knowledge of the landscape, experience with various models, and the ability to advise on model suitability and total cost of ownership. Advanced hands-on engineering skills in Python are essential, along with experience in knowledge AI systems and their application to both client-facing and internal development.

Qualifications

• Demonstrated experience architecting and overseeing enterprise-scale knowledge AI systems.
• Expertise in designing and governing enterprise agentic AI frameworks and LLM Ops standards.
• Experience integrating external vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms.
• Ability to advise clients and internal stakeholders on multi-year AI architecture strategy, including platform investment decisions and build-vs-operate trade-offs.
• Experience defining enterprise-wide evaluation and observability standards for agentic and LLM systems.

Skills

• Gen AI Foundational: Ability to translate complex enterprise business challenges into strategic AI architecture decisions, balancing immediate delivery needs with long-term platform scalability and firmwide adoption.
• Advanced hands-on engineering credibility in Python.
• Expertise in designing and governing enterprise agentic AI frameworks including multi-agent orchestration, tool use patterns, and memory architecture.
• Experience integrating external vendor tooling for model monitoring, observability, safety, and compliance.
• Ability to define and enforce LLM Ops standards across the enterprise.
• Experience defining enterprise-wide evaluation and observability standards for agentic and LLM systems.
• Software Engineering: Ability to define and govern API strategy, containerization, and integration standards for enterprise AI platforms.
• Proven track record of building and delivering large-scale enterprise AI platforms.
• Experience governing data security, privacy, and compliance practices.
• Strong ability to communicate complex AI architecture concepts to executive, technical, and non-technical audiences.

Benefits

At EY, you'll develop future-focused skills and receive world-class experiences. You'll be empowered in a flexible environment and fueled by your extra drive and ambition.

Pay

TBD

Schedule

TBD