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

EY•Charlotte, NC

39d•$144,000 - $374,000•Hybrid

About The Position

EY is hiring a Senior Manager to lead the architecture and delivery of complex AI/ML initiatives for financial services clients and across the organization. The role sets AI system design principles, reference architectures, engineering standards, and long-term technology direction, while maintaining hands-on technical credibility and accountability for architecture, integration, delivery, quality, and operational excellence. It translates business and user requirements into scalable, secure, reusable technical designs that can be adopted across teams and domains. The position covers enterprise AI systems including generative AI, retrieval-augmented generation (RAG), agentic systems, knowledge AI, NLP, and multimodal applications. The Senior Manager shapes enterprise integration and API strategies, establishes evaluation and observability practices, and promotes responsible AI, security, resilience, and regulatory compliance. The role also leads complex program outcomes, advises on platform strategy and investment, works with senior cross-functional stakeholders, and mentors senior engineers and technical leads. The role is based in Charlotte, Dallas, New York, or Tampa. EY describes its work model as team-led and leader-enabled hybrid, with most external client-serving employees expected to work together in person 40–60% of the time over an engagement, project, or year.

Requirements

10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles.
Advanced hands-on engineering credibility in Python.
Deep knowledge of open-source and commercial foundation models and ability to evaluate model suitability, capability trade-offs, and total cost of ownership.
Demonstrated experience architecting and overseeing enterprise-scale knowledge AI systems spanning foundation model management, agentic design, AI application integration, and NLP or multimodal systems.
Expertise designing and governing enterprise agentic AI frameworks, including multi-agent orchestration, tool-use patterns, and memory architecture.
Ability to define and enforce LLM operations standards, including model lifecycle governance, deployment pipelines, versioning, and continuous improvement.
Experience integrating vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms.
Ability to define and govern API strategy, containerization, and integration standards for enterprise AI platforms, including security, reliability, and decoupling consumers from internal model and workflow topology.
Track record of building and delivering large-scale enterprise AI platforms while balancing technical contribution, cross-functional coordination, and stakeholder alignment.
Experience governing data security, privacy, and compliance practices for enterprise LLM and agentic system development and deployment.
Ability to explain complex AI system behavior and trade-offs to technical and non-technical stakeholders, including risk and compliance audiences.
Strong ownership and accountability for AI systems from design through production and issue resolution.
Comfort operating amid ambiguity as requirements, regulations, and technologies evolve.
Collaborative approach with engineering, product, risk, legal, and audit teams.
Sound judgment in regulated environments, with awareness of risk, controls, and when human oversight is required.

Nice To Haves

Bachelor’s degree preferred.
Master’s degree in Business Administration (MBA) or Science (MS) preferred.
Prior consulting experience.
Experience advising clients on enterprise AI platform strategy, build-versus-operate trade-offs, vendor evaluation, and integration of foundation-model and agentic tools into existing technology ecosystems.
Ability to translate governance and compliance requirements into scalable technical architectures that enable responsible AI adoption.
Ability to maintain current knowledge of emerging AI techniques, model architectures, and agentic patterns and assess enterprise readiness and applicability.
Ability to collaborate across business, technology, and product domains to align AI initiatives with enterprise architecture standards and strategic objectives.
Familiarity with LLM safety and alignment techniques, including output filtering, guardrail design, and responsible AI governance frameworks.
Familiarity with multimodal and vision-language model architectures and enterprise knowledge AI use cases.
Familiarity with small language model design patterns for 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 supporting large-scale AI systems, including data mesh, lakehouse, and real-time streaming pipelines.
Familiarity with AI-assisted software engineering tools for enterprise-scale design, implementation, and review.
Experience with GPU-accelerated AI workloads and cloud AI services, and 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.

Responsibilities

Define and govern design principles, reference architectures, and engineering patterns for AI/ML, generative AI, RAG, and agentic systems.
Lead escalated technical challenges across 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 engineering standards and practices for design reviews, architecture governance, observability, resilience, security, and responsible AI.
Shape how AI/ML components integrate with product, platform, infrastructure, and client delivery ecosystems.
Define and evolve API and integration strategies, including contract design, versioning, security, idempotency, and reliability patterns.
Ensure API and application integration patterns decouple consumers from internal AI service topology so models, workflows, and data stores can evolve safely.
Lead complex project and program delivery by aligning architecture, engineering execution, stakeholder governance, risks, dependencies, and delivery quality.
Influence platform strategy, technical roadmaps, and investment decisions using engineering judgment and delivery insight.
Partner with leaders in Engineering, Architecture, Product, Data, Security, Operations, and engagement teams to align strategy and execution.
Establish scalable model evaluation, benchmarking, experimentation, rollout controls, and production quality measurement.
Mentor senior engineers and technical leads and strengthen system design, technical depth, delivery rigor, and architectural decision-making.
Use AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms to accelerate design, implementation, and review.
Identify opportunities to reduce duplication, accelerate delivery, and build reusable AI platform capabilities across the enterprise.
Advise clients and internal stakeholders on enterprise AI architecture strategy, platform investment, build-versus-operate trade-offs, and sequencing of AI capabilities.
Establish enterprise-wide evaluation and observability standards for output quality, behavioral drift, safety, and auditability.
Communicate complex AI architecture concepts to executive, technical, and non-technical audiences and translate strategic direction into engineering roadmaps.