Vice President, Generative AI Platform Engineering

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Job Description:

Note: Fidelity will not provide immigration sponsorship for this position

The Role

As the Vice President of Generative AI Platform Engineering, you will lead the architecture, engineering, delivery, and operations of the firm’s enterprise Generative AI Platform. You will be responsible for building the foundational technology capabilities that enable secure, scalable, and compliant, and governed by the adoption of AI Agents, LLM-powered applications, and agentic workflows across the enterprise.

Reporting to the SVP, Head of AI/ML Technology, this leader will establish and execute the engineering strategy for a next-generation AI Control Plane that enables model access, agent orchestration, tool integration, governance, identity management, observability, evaluation, and compliance monitoring at enterprise scale.

You will lead teams of software engineers, platform engineers, and ML engineers responsible for designing and operating shared capabilities that support thousands of developers, hundreds of AI-enabled applications, and mission-critical business workflows.

Success in this role requires deep expertise in distributed systems, AI platform engineering, cloud-native architecture, GenAI technologies, agent frameworks, AI governance, and enterprise-scale operational excellence.

Key Responsibilities

• Partner closely with the SVP, Head of AI/ML Technology to execute the long-term enterprise AI vision.

• Translate strategic AI objectives into scalable platform capabilities and engineering roadmaps.

• Own the architecture, delivery, and continuous evolution of Fidelity’s enterprise Generative AI Platform and its AI Control Plane which will be the single, governed layer through which every LLM application, AI agent, and agentic workflow across the firm is provisioned, secured, observed, and controlled.

• Define the platform’s reference architectures, technical standards, and “golden paths” which will be opinionated, pre-approved patterns for building, deploying, and operating GenAI and agentic applications

• Establish the engineering practices (design review, testing, release management) and operational processes (capacity, cost, change, and incident management) that keep the platform reliable at enterprise scale.

• Offer reusable building blocks such as RAG pipelines, vector stores, a governed tool/connector catalog, and memory services, as managed, self-service capabilities

• Own end-to-end agent lifecycle management: an agent and tool registry, least-privilege capability boundaries, session state and memory, versioning, and rollback and incident-response mechanisms for AI workflows and agents.

• Establish unified identity and access management for both human and non-human (agent) identities across the platform.

• Deliver deep observability: end-to-end tracing of agent reasoning and tool calls, telemetry, quality and drift monitoring, and cost and latency dashboards.

• Build and lead a high-performing team of engineers, architects, engineering managers, ML engineers, and product leaders.

• Create a culture of innovation, accountability, craftsmanship, and operational excellence.

• Recruit and develop world-class engineering talent.

Required Qualifications

• Bachelor’s degree in computer science, Engineering, Mathematics, or related field.

• Advanced degree preferred.

• 15+ years of technology leadership experience.

• 10+ years leading large-scale software engineering organizations.

• 3+ years building cloud-native AI/ML platforms.

• Experience delivering enterprise GenAI and agentic AI platforms.

• Hands-on experience building AI solutions, agentic workflows, AI agents, and LLM-powered applications.

• Experience with a good understanding of  two or more of the following: LangGraph, CrewAI, OpenAI, Anthropic, Amazon Bedrock, Azure AI Foundry, or comparable emerging agent frameworks.

• Strong understanding of LLMs and AI orchestration platforms, model capabilities and limitations, context management, token economics, and cost/latency trade-offs.

• Good understanding of RAG architecture with a strong focus on retrieval — chunking and embedding strategies, vector databases, hybrid search, re-ranking, and evaluating retrieval quality.

• Solid understanding of the Model Context Protocol (MCP), including its current limitations and security considerations.

• Experience operating Kubernetes as the foundation of AI infrastructure is a huge plus. Especia…