Head of AI & Agentic Platform Engineering

About the role

The Head of AI & 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.

Responsibilities

• Gateway & Serving:

  • 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.
  • Model serving infrastructure, low-latency inference, auto-scaling, and multi-region deployment for production models.
  • Agentic AI runtime, the infrastructure layer that supports autonomous AI agents taking multi-step actions across Pfizer's systems.

• Compute & Environments:

  • Enterprise compute provisioning, GPU, TPU, and CPU infrastructure across cloud and on-premises, including capacity planning, FinOps governance, and utilization optimization.
  • Pre-configured AI environments, reproducible, governed workspaces that enable data scientists to focus on scientific problems, not infrastructure.
  • Infrastructure as Code, automated, auditable environment provisioning across development, staging, and production.
  • HPC support, infrastructure capable of supporting large-scale scientific simulation and molecular modeling workloads (preferred, not required).

• Runtime Enablement:

  • 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.
  • Production observability, monitoring, alerting, and dashboarding for AI systems in production: latency, throughput, drift detection, and model health.
  • Developer experience, APIs, SDKs, and documentation that enable federated teams to deploy production models without deep infrastructure expertise.

• Registry, Deploy & Trust:

  • Enterprise AI model registry, the authoritative record of every AI model and agent in development, staging, and production across Pfizer, including metadata, version history, risk tier, Trusted AI validation status, ownership, and complete audit trail.
  • Deployment pipeline infrastructure, automated pipelines through which models and agents move from development to staging to production, with Trusted AI sign-off gates enforced as first-class pipeline steps.
  • Includes release management, canary deployments, A/B testing, and rapid rollback capabilities.
  • Production monitoring and drift detection, continuous observation of AI system performance in production: prediction quality, output distributions, latency, throughput, and drift.
  • For agentic systems, monitoring extends to agent behavior, action sequences, tool usage, decision consistency, and anomalous behavior detection.
  • Guardrails and policy enforcement, the technical implementation of Trusted AI's governance policies as executable runtime controls: input/output filtering, PII detection, agent action controls, permission scoping, circuit breakers, and prompt injection defenses designed in partnership with the CISO organization.
  • GxP-compliant audit trail, complete, tamper-evident logging of every deployment event, configuration change, and model transition, meeting the documentation standards required for AI systems operating in regulated pharmaceutical environments.

Qualifications

• 12+ years in software or infrastructure engineering, with 7+ years in AI/ML platform, MLOps, or AI infrastructure roles at significant scale.
• 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.
• 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.

Preferred Qualifications

• 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.