Head of AI & Agentic Platform Engineering

<div><p><b>ROLE SUMMARY</b></p><p>The Head of AI &amp; Agentic Platform Engineering owns the infrastructure layer that makes Pfizer's AI ambitions executable, the compute, LLM gateway, MLOps machinery, and observability platform on which every AI workload at Pfizer runs. This is not a supporting function. It is the capability that determines whether Pfizer's AI strategy moves at the speed of ambition or the speed of infrastructure constraints. The platform this team builds is the difference between a data scientist who spends two weeks provisioning an environment and one who is running experiments on day one, and between an AI model that takes six months to reach production and one that ships in days through a governed, automated deployment pipeline.</p></div><p></p><p>The scope of AI workloads this platform must support is broad. Each Pfizer domain (i.e., R&amp;D, Commercial, Global Supply, Enabling Functions) has distinct compute, latency, governance, and reliability requirements, and this platform must serve all of them without compromise. As Pfizer advances from assistive AI tools toward autonomous agentic systems that take multi-step actions across the enterprise, the demands on this platform will grow in both complexity and consequence. The LLM gateway, agent orchestration layer, and observability infrastructure this leader builds today must be architected for that future from the outset.</p><p></p><p>The team of engineers is organized across four pods, LLM Gateway &amp; Model Serving, Compute &amp; Environments, Runtime Enablement and Registry, Deploy &amp; Trust, each owning a distinct and critical layer of the AI infrastructure stack including agent lifecycle management.</p><p></p><p></p><p><b>ROLE RESPONSIBILITIES</b></p><p></p><p><b>Gateway &amp; Serving</b></p><ul><li>Enterprise LLM gateway, access control, multi-model routing, rate limiting, cost attribution, and audit logging for all LLM interactions across Pfizer, including agentic AI workloads.</li><li>Model serving infrastructure, low-latency inference, auto-scaling, and multi-region deployment for production models.</li><li>Agentic AI runtime, the infrastructure layer that supports autonomous AI agents taking multi-step actions across Pfizer's systems. This is meaningfully different from stateless LLM inference: agents require stateful process management, short-term and long-term memory, tool-calling orchestration, and the ability to coordinate with other agents. As Pfizer's agentic AI portfolio grows, this layer becomes one of the most strategically critical components of the platform. The Head of AI &amp; Agentic Platform Engineering is expected to architect this capability proactively, not wait for agent use cases to arrive and then retrofit the infrastructure.</li><li>Gateway observability, real-time usage monitoring, cost attribution by team and use case, and anomaly detection. Enterprise tool and MCP registry, the governed catalog of tools, APIs, and data sources that AI agents are permitted to call at runtime. As the number of agent-callable tools grows across Pfizer, this registry becomes the mechanism by which the platform enforces what agents can do, not just what they can say. Built and maintained in close partnership with the Trusted AI team's agent governance function.</li></ul><p></p><p><b>Compute &amp; Environments</b></p><ul><li>Enterprise compute provisioning, GPU, TPU, and CPU infrastructure across cloud and on-premises, including capacity planning, FinOps governance, and utilization optimization.</li><li>Pre-configured AI environments, reproducible, governed workspaces that enable data scientists to focus on scientific problems, not infrastructure.</li><li>Infrastructure as Code, automated, auditable environment provisioning across development, staging, and production.</li><li>HPC support, infrastructure capable of supporting large-scale scientific simulation and molecular modeling workloads (preferred, not required).</li></ul><p></p><p><b>Runtime Enablement</b></p><ul><li>MLOps platform, experiment tracking, model versioning, automated evaluation, deployment pipelines, and model registry, with integration into Trusted AI's risk classification and sign-off process.</li><li>Production observability, monitoring, alerting, and dashboarding for AI systems in production: latency, throughput, drift detection, and model health.</li><li>Developer experience, APIs, SDKs, and documentation that enable federated teams to deploy production models without deep infrastructure expertise.</li></ul><p></p><p><b>Registry, Deploy &amp; Trust</b></p><p>This pod was previously a standalone Trust Engineering team. Its integration into AI &amp; Agentic Platform Engineering reflects a deliberate architectural decision: agent lifecycle management, register, deploy, monitor, govern, retire, is infrastructure, and the operational boundary between deploying a model and operating an agent has collapsed. The pod owns:</p><ul><li>Enterprise AI model r