Head of AI & Agentic Platform Engineering

Pfizer • New York City, NY

1d • Hybrid

About The Position

The Head of AI & Agentic Platform Engineering owns the infrastructure layer that makes Pfizer's AI ambitions executable, including the compute, LLM gateway, MLOps machinery, and observability platform on which every AI workload at Pfizer runs. This role is critical for determining the speed at which Pfizer's AI strategy can be executed, distinguishing between rapid experimentation and slow infrastructure provisioning. The platform aims to enable data scientists to run experiments on day one and deploy AI models in days through a governed, automated pipeline. The platform must support a broad scope of AI workloads across Pfizer's domains (R&D, Commercial, Global Supply, Enabling Functions), each with distinct compute, latency, governance, and reliability requirements. As Pfizer moves towards autonomous agentic systems, the platform's complexity and consequence will grow, requiring proactive architecture for the LLM gateway, agent orchestration layer, and observability infrastructure to support future demands.

Requirements

• 12+ years in software or infrastructure engineering
• 7+ years in AI/ML platform, MLOps, or AI infrastructure roles at significant scale
• Demonstrated experience building and operating multi-tenant AI/ML platform infrastructure, compute provisioning, model training pipelines, model serving, and production monitoring
• Deep hands-on experience with LLM gateway or model serving infrastructure, multi-model routing, inference optimization, access control, and cost attribution at enterprise scale
• Proven MLOps platform experience with documented outcomes in deployment velocity, reliability, and developer satisfaction
• Strong IaC practices in a multi-cloud architecture (Azure, AWS, GCP including Terraform expertise)
• Experience leading platform teams with an SLA-driven, product-minded operating model
• Demonstrated ability to collaborate across organizational boundaries, with adjacent platform teams, security functions, and governance stakeholders
• Ability to translate infrastructure architecture and trade-offs for both technical teams and senior business stakeholders
• Experience with encryption and security tools, techniques, and best practices
• Experience operating AI infrastructure in a regulated environment with GxP controls, audit trail requirements, and validated environment obligations
• Breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact
• Permanent work authorization in the United States

Nice To Haves

• Experience building or operating ML platform infrastructure at a major technology company (Google, Meta, Microsoft, OpenAI, or equivalent) at petabyte scale with thousands of concurrent ML engineers
• Experience designing agentic AI infrastructure, specifically the orchestration layer, memory architecture (short-term context, long-term persistent memory), tool-calling and MCP integration, agent-to-agent communication, and the safety architecture required to constrain autonomous agents operating in production
• Candidates who have built or operated agent runtimes at scale, whether in a research or product context, will be strongly preferred
• Deep LLM-specific infrastructure experience: KV cache management, speculative decoding, quantization trade-offs, and concurrent multi-model serving
• HPC environment experience, job schedulers (SLURM, LSF, or equivalent), parallel file systems, and large-scale scientific compute workloads

Responsibilities

• Manage the Enterprise LLM gateway, including access control, multi-model routing, rate limiting, cost attribution, and audit logging for all LLM interactions, including agentic AI workloads.
• Oversee model serving infrastructure, ensuring low-latency inference, auto-scaling, and multi-region deployment for production models.
• Develop and manage the Agentic AI runtime infrastructure, supporting autonomous AI agents with stateful process management, memory, tool-calling orchestration, and agent coordination.
• Architect the LLM gateway observability, including real-time usage monitoring, cost attribution, and anomaly detection.
• Manage the Enterprise tool and MCP registry, a governed catalog of tools, APIs, and data sources for AI agents, ensuring platform enforcement of agent capabilities.
• Oversee enterprise compute provisioning, including GPU, TPU, and CPU infrastructure across cloud and on-premises, capacity planning, FinOps governance, and utilization optimization.
• Provide pre-configured AI environments that are reproducible and governed, enabling data scientists to focus on scientific problems.
• Implement and maintain Infrastructure as Code for automated, auditable environment provisioning across development, staging, and production.
• Support HPC environments for large-scale scientific simulation and molecular modeling workloads (preferred).
• Manage the MLOps platform, including experiment tracking, model versioning, automated evaluation, deployment pipelines, and model registry integration with Trusted AI's risk classification.
• Ensure production observability for AI systems, including monitoring, alerting, and dashboarding for latency, throughput, drift detection, and model health.
• Enhance developer experience through APIs, SDKs, and documentation for federated teams.
• Manage the Enterprise AI model registry, serving as the authoritative record of AI models and agents, including metadata, version history, risk tier, and audit trail.
• Oversee the deployment pipeline infrastructure for automated model and agent deployment, enforcing Trusted AI sign-off gates.
• Implement continuous observation of AI system performance in production, including prediction quality, output distributions, latency, throughput, and drift.
• Manage guardrails and policy enforcement, including input/output filtering, PII detection, agent action controls, and prompt injection defenses.
• Ensure a GxP-compliant audit trail for all deployment events, configuration changes, and model transitions.

Benefits

• 401(k) plan with Pfizer Matching Contributions
• Additional Pfizer Retirement Savings Contribution
• Paid vacation, holiday and personal days
• Paid caregiver/parental and medical leave
• Health benefits to include medical, prescription drug, dental and vision coverage
• Relocation assistance may be available

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: No Education Listed