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 bringing deep technical authority, exceptional hands-on engineering experience, and the ability to shape long-term technology direction while also leading complex project and program outcomes.

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 ideal candidate should have a passion for solving complex challenges in the financial services industry, a Bachelor’s degree preferred, and 10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles. Demonstrated experience in the following areas will be a huge plus:

• Demonstrated experience advising clients on enterprise AI platform strategy including build-vs-operate trade-offs, vendor evaluation, and integration of foundational model and agentic tooling into existing technology ecosystems.
• Demonstrated ability to translate governance and compliance requirements into scalable technical architectures that enable responsible AI adoption at scale.
• Experience with emerging AI techniques, model architectures, and agentic patterns and assessing their readiness and applicability for enterprise adoption.
• Experience collaborating across business, technology, and product domains to align AI initiatives with enterprise architecture standards and strategic objectives.
• Familiarity with safety and alignment techniques for large language models including output filtering, guardrail design, and responsible AI governance frameworks.
• Familiarity with multimodal and vision-language model architectures and their applicability to enterprise knowledge AI use cases.
• Familiarity with small language model design patterns and their role in cost-effective, latency-sensitive enterprise deployments.
• Familiarity with AI regulatory and risk management frameworks relevant to financial services including model risk governance and explainability obligations.
• Familiarity with enterprise data architecture patterns that underpin large-scale AI systems including data mesh, datalake house, and real-time streaming pipelines.
• Familiarity with AI-assisted software engineering tools for accelerating engineering design, implementation, and review practices at enterprise scale.
• Experience with GPU-accelerated AI workloads and cloud AI services, and the ability to advise on infrastructure strategy for model training and inference at scale.
• Experience establishing enterprise engineering standards, architecture governance practices, and AI platform modernization initiatives.

Qualifications

A clear communicator able to explain complex AI system behavior and trade-offs to technical and non-technical stakeholders, including risk and compliance. Strong ownership and accountability, taking responsibility for AI systems from design through production and issue resolution. Comfort with ambiguity, able to operate effectively as requirements, regulations, and technologies evolve. Collaborative and cross-functional, working closely with engineering, product, risk, legal, and audit teams. Sound judgment in regulated environments, with awareness of risk, controls, and when human oversight is required.

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, with the ability to guide architecture and implementation decisions across senior engineering teams.
• Expertise in designing and governing enterprise agentic AI frameworks including multi-agent orchestration, tool use patterns, and memory architecture across complex, large-scale deployments.
• Experience integrating external vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms.
• Ability to define and enforce LLM Ops standards across the enterprise including model lifecycle governance, deployment pipelines, versioning strategies, and continuous improvement frameworks.
• Ability to advise clients and internal stakeholders on multi-year AI architecture strategy, including platform investment decisions, build-vs-operate trade-offs, and sequencing of AI capability development across the enterprise.
• Experience defining enterprise-wide evaluation and observability standards covering output quality, behavioral drift, safety, and auditability across agentic and LLM systems.
• Software Engineering: Ability to define and govern API strategy, containerization, and integration standards for enterprise AI platforms including contract design, versioning, security, idempotency, and reliability patterns — ensuring AI service consumers are decoupled from internal model and workflow topology.
• Proven track record of building and delivering large-scale enterprise AI platforms, balancing hands-on technical contribution with cross-functional coordination and stakeholder alignment.
• Experience governing data security, privacy, and compliance practices as they apply to enterprise LLM and agentic system development and deployment.
• Strong ability to communicate complex AI architecture concepts to executive, technical, and non-technical audiences and translate strategic direction into actionable engineering roadmaps.

Benefits

At EY, we offer a competitive compensation package, comprehensive benefits, and a supportive work environment. We provide opportunities for professional growth and development, as well as a range of perks and incentives to enhance your well-being and satisfaction.

Pay

Competitive compensation based on experience and qualifications.

Schedule

Full-time position.