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

Location: Charlotte, Dallas, New York, Tampa Forward Deployed Engineer – Applied AI - Senior Manager - Financial Services EY’s Financial Services Organisation (FSO) is a dedicated unit that partners with financial institutions to solve complex challenges using AI and advanced technology. As a Senior Manager, you will lead the architecture and delivery of AI‑enabled solutions that transform business operations, drive innovation, and enable new revenue streams. The Opportunity Leads the definition and delivery of AI system design principles, reference architectures, and engineering standards for highly complex AI/ML initiatives. Brings deep technical authority and hands‑on engineering experience, shaping long‑term technology direction while managing complex projects and programs. Your Key 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. Use modern AI‑assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms to accelerate engineering design, implementation, and review practices. Identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities across the enterprise. AI and Engineering Skills Gen AI Foundational Translate complex enterprise business challenges into strategic AI architecture decisions, balancing immediate delivery needs with long‑term platform scalability and firm‑wide adoption. Deep knowledge of foundation model landscape, including open‑source and commercial models, and ability to evaluate, select, and advise on model suitability, capability trade‑offs, and total cost of ownership across diverse enterprise use cases. Demonstrated experience architecting and overseeing enterprise‑scale knowledge AI systems spanning foundation model management, agentic design, AI application integration, and NLP and multimodal systems. Advanced hands‑on engineering credibility in Python, guiding architecture and implementation decisions across senior engineering teams. Subject‑matter authority on knowledge AI systems and their application to client‑facing opportunities and internal platform development. Agentic and LLM Ops Design and govern enterprise agentic AI frameworks, multi‑agent orchestration, tool‑use patterns, and memory architecture across complex, large‑scale deployments. Define and enforce LLM Ops standards across the enterprise, including model lifecycle governance, deployment pipelines, versioning strategies, and continuous improvement frameworks. Integrate external vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms. Advise clients and stakeholders on multi‑year AI architecture strategy, platform investment decisions, build‑vs‑operate trade‑offs, and sequencing of AI capability development. Identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities and reference architectures. Define enterprise‑wide evaluation and observability standards covering output quality, behavioral drift, safety, and auditability across agentic and LLM systems. Software Engineering Define and govern API strategy, containerization, and integration standards for enterprise AI platforms, ensuring AI service consumers are decoupled from internal model and workflow topology. Build and deliver large‑scale enterprise AI platforms, balancing hands‑on technical contribution with cross‑functional coordination and stakeholder alignment. Govern data security, privacy, and compliance practices as they apply to enterprise LLM and agentic system development and deployment. Communicate complex AI architecture concepts to executive, technical, and non‑technical audiences and translate strategic direction into actionable engineering roadmaps. Soft Skills and Additional Attributes for Success 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, operating 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. Bachelor’s degree preferred. 10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles. Demonstrated Experience that Will Be a Huge Plus 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. Translating governance and compliance requirements into scalable technical architectures that enable responsible AI adoption at scale. Keeping current knowledge of emerging AI techniques, model architectures, and agentic patterns and assessing their readiness for enterprise adoption. 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 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. Experience 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, enabling infrastructure strategy for model training and inference at scale. Experience establishing enterprise engineering standards, architecture governance practices, and AI platform modernization initiatives. Ideally, You’ll Also Have Master’s degree in Business Administration (MBA) or Science (MS). Prior consulting experience. What We Offer You Competitive compensation and benefits package, including medical and dental coverage, pension and 401(k) plans, and a range of paid time‑off options. Hybrid work model with flexibility for remote and in‑office collaboration. Opportunities to work on high‑impact projects across a global network of teams and clients. Equal Employment Opportunity EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity or expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law. EY is committed to providing reasonable accommodation to qualified individuals with disabilities. #J-18808-Ljbffr EY