Director, Applied AI

Pfizer Pfizer · Pharma · New York, NY

Director of Applied AI at Pfizer, responsible for translating AI strategy into production-grade solutions, platforms, and products. This role involves end-to-end technical ownership, leading engineering teams, and ensuring solutions are robust, scalable, and aligned with enterprise architecture, with a focus on business outcomes. Experience with LLMs, retrieval architectures, AI agents, observability, and governance is preferred.

What you'd actually do

  1. Translate strategic intent and architectural patterns into working AI solutions and platforms. Own the end-to-end architecture and implementation shape of major solutions, ensuring they are production-ready, scalable, maintainable, and aligned with enterprise architecture.
  2. Lead technical design and execution across complex initiatives as architect, tech lead, and delivery driver. Contribute hands-on code where it accelerates delivery and de-risks execution, and guide teams through design decisions, implementation trade-offs, and integration challenges.
  3. Ensure seamless integration across platforms and services, data systems, AI models, and business workflows — connecting models to real use cases, guardrails to runtime systems, and observability to operational feedback loops within enterprise constraints.
  4. Lead and coordinate a delivery team of full-time engineers, contractors, and vendor resources. Direct day-to-day technical execution, review their work to Pfizer's quality and security bar, manage dependencies and throughput, and grow the capability of the people you lead.
  5. Identify and challenge incomplete or fragile architectures, solutions that are not production-ready, and unrealistic assumptions. Enforce engineering discipline in scalability, observability, performance, and security so solutions meet enterprise quality and reliability standards.

Skills

Required

  • 10+ years in software engineering, AI/ML, or architecture roles
  • strong delivery track record
  • building production-grade AI/ML systems or platforms
  • hands-on coding ability
  • depth across AI/ML and generative AI
  • depth across system design / architecture
  • depth across data engineering
  • depth across cloud engineering
  • leading complex technical initiatives end-to-end
  • leading or coordinating engineers, contractors, and vendors
  • connecting technical decisions to business and commercial outcomes
  • operating in cross-functional, matrixed environments

Nice to have

  • LLMs
  • retrieval architectures
  • AI agents
  • observability and monitoring
  • AI governance and guardrails
  • Claude Code
  • Claude Enterprise
  • MCP development
  • Claude Skill Development
  • biopharma TA role
  • epidemiology
  • real-world data experience
  • enterprise-scale or regulated environments
  • leading platform or large solution-development efforts
  • CI/CD
  • DevOps
  • production operations

What the JD emphasized

  • production-grade AI/ML systems
  • end-to-end technical integrity
  • production-ready
  • enterprise architecture
  • enterprise systems
  • enterprise constraints
  • enterprise quality and reliability standards
  • enterprise-scale or regulated environments

Other signals

  • production-grade AI/ML systems
  • end-to-end technical integrity
  • translate strategy into systems
  • lead mixed teams