Key skills
Artificial IntelligenceMachine LearningGenerative AIRetrieval-Augmented Generation (RAG)Agentic AI SystemsFoundation Models e.g. GPT-4oFoundation Models ClaudeFoundation Models LlamaFoundation Models GeminiFoundation Models MistralPythonKnowledge AI SystemsNatural Language Processing (NLP)Multimodal AI SystemsAzure OpenAIAWS BedrockGoogle Vertex AIHugging FaceMulti-agent OrchestrationLangGraphAutoGenSemantic KernelNVIDIA NIMCrewAILLM OperationsMLflowAzure MLKubeflowGitHub ActionsLangChainLlamaIndexModel Monitoring and ObservabilityLangSmithArizeDatadogAzure MonitorAI Architecture StrategyModel Evaluation and BenchmarkingDockerKubernetesREST APIsgRPCAzure API ManagementAWS API GatewayData Security and PrivacyAzure PurviewAWS MacieMicrosoft PresidioResponsible AI PracticesNeMo GuardrailsGuardrails AIAzure Content SafetyLLM GuardData MeshDatalake HouseReal-time Streaming PipelinesDatabricksAzure SynapseApache KafkaDelta LakeAI-assisted Software Engineering ToolsClaude CodeGitHub CopilotCodexGPU-accelerated AI WorkloadsNVIDIA GPU PlatformsNVIDIA AI EnterpriseAWS SageMakerEnterprise AI Platform StrategyModel Risk GovernanceExplainability ObligationsFinancial Services Regulatory Frameworks e.g. SR 11-7Financial Services Regulatory Frameworks FINRAFinancial Services Regulatory Frameworks REG BIAIMLNLPLLMLarge Language ModelsOpenAIClaudeGeminiLlamaRAGAgenticBIKafkaAWSAzureSageMakerBedrockAPI GatewayVertex AIGitHubLeadershipRisk Management
About this role
EY is the only professional services firm with a separate business unit dedicated to the financial services marketplace. The role involves leading the definition and delivery of AI system design principles and engineering standards for complex AI/ML initiatives, ensuring technical quality and operational excellence of AI-enabled solutions.
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
Requirements:
- Ability to translate complex enterprise business challenges into strategic AI architecture decisions, balancing immediate delivery needs with long-term platform scalability and firmwide adoption
- Deep knowledge of foundation model landscape including open-source and commercial models, with the ability to evaluate, select, and advise on model suitability, capability trade-offs, and total cost of ownership across diverse enterprise use cases (e.g. GPT-4o, Claude, Llama, Gemini, Mistral etc.)
- Demonstrated experience architecting and overseeing enterprise-scale knowledge AI systems spanning foundation model management, agentic design, AI application integration, and NLP and multimodal systems (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face etc.)
- Advanced hands-on engineering credibility in Python, with the ability to guide architecture and implementation decisions across senior engineering teams
- Experience serving as a subject matter authority on knowledge AI systems and their application to both client-facing opportunities and internal horizontal platform development
- Expertise in designing and governing enterprise agentic AI frameworks including multi-agent orchestration, tool use patterns, and memory architecture across complex, large-scale deployments (e.g. LangGraph, AutoGen, Semantic Kernel, NVIDIA NIM, CrewAI etc.)
- Ability to define and enforce LLM Ops standards across the enterprise including model lifecycle governance, deployment pipelines, versioning strategies, and continuous improvement frameworks (e.g. MLflow, Azure ML, Kubeflow, GitHub Actions, LangChain, LlamaIndex etc.)
- Experience integrating external vendor tooling for model monitoring, observability, safety, and compliance into enterprise AI platforms (e.g. LangSmith, Arize, Datadog, Azure Monitor etc.)
- 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
- Ability to identify opportunities to reduce duplication, accelerate delivery, and create reusable AI platform capabilities and reference architectures that can be adopted across multiple teams and client engagements
- Experience defining enterprise-wide evaluation and observability standards covering output quality, behavioral drift, safety, and auditability across agentic and LLM systems (e.g. RAGAS, DeepEval, Arize, Weights & Biases etc.)
- 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 (e.g. Docker, Kubernetes, REST, gRPC, Azure API Management, AWS API Gateway etc.)
- 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 (e.g. Azure Purview, AWS Macie, Microsoft Presidio etc.)
- Strong ability to communicate complex AI architecture concepts to executive, technical, and non-technical audiences and translate strategic direction into actionable engineering roadmaps
- 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
- Bachelor's degree preferred
- 10+ years of applied engineering experience, including extensive experience in senior AI/ML engineering, architecture, or complex technology delivery roles
- Master's degree in Business Administration (MBA) or Science (MS) preferred
- Prior consulting experience
- 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 (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI, NVIDIAAI Enterprise, Hugging Face etc.)
- Demonstrated ability to translate governance and compliance requirements into scalable technical architectures that enable responsible AI adoption at scale
- Ability to maintain current knowledge of emerging AI techniques, model architectures, and agentic patterns and assess their readiness and applicability for enterprise adoption
- Ability to collaborate 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 (e.g. NeMo Guardrails, Guardrails AI, Azure Content Safety, LLM Guard etc.)
- Familiarity with multimodal and vision-language model architectures and their applicability to enterprise knowledge AI use cases (e.g. GPT-4o, Gemini, LLaVA etc.)
- Familiarity with small language model design patterns and their role in cost-effective, latency-sensitive enterprise deployments (e.g. Phi, Mistral, Llama 3, Gemma etc.)
- Familiarity with AI regulatory and risk management frameworks relevant to financial services including model risk governance and explainability obligations (e.g. SR 11-7, FINRA, REG BI)
- Familiarity with enterprise data architecture patterns that underpin large-scale AI systems including data mesh, datalake house, and real-time streaming pipelines (e.g. Databricks, Azure Synapse, Apache Kafka, Delta Lake etc.)
- Familiarity with AI-assisted software engineering tools for accelerating engineering design, implementation, and review practices at enterprise scale (e.g. Claude Code, GitHub Copilot, Codex etc.)
- 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 (e.g. NVIDIA GPU platforms, NVIDIA AI Enterprise, Azure ML, AWS SageMaker etc.)
- Experience establishing enterprise engineering standards, architecture governance practices, and AI platform modernization initiatives