Director, AI Engineering--clinical Development and Operations (cd&o)

Pfizer Pfizer · Pharma · New York, NY

This role focuses on designing, building, and deploying production-grade AI systems, particularly LLMs and agentic AI, for clinical development and operations within a regulated healthcare environment. It involves developing predictive models, engineering robust ML pipelines, and implementing agentic solutions, with a strong emphasis on MLOps and cloud deployment.

What you'd actually do

  1. Build and deploy AI/ML models and solutions that support process-heavy workflows (e.g. protocol feasibility and site selection, study start-up etc.) including documentation, and operational reporting.
  2. Develop predictive, optimization, and scenario-based models to support clinical trial supply forecasting and operational planning.
  3. Implement AI solutions that are aligned with data integrity standards and governance best practices, including model validation, versioning, and monitoring.
  4. Design and implement AI agentic solutions that can plan and execute multi-step workflows.
  5. Build robust, production-ready ML and analytics pipelines with a focus on reproducibility and scalability.

Skills

Required

  • PhD or Master's degree in a relevant field
  • 5-7 years of applied analytical experience
  • Hands-on experience applying LLMs, generative AI, machine learning
  • Experience building practical, reusable workflows or systems
  • Strong implementation skills in Python
  • Modern AI / ML tooling
  • Sound judgment regarding methodological rigor, model limitations, evaluation, and human oversight
  • Experience working directly with domain users or stakeholders
  • Strong collaboration and communication skills
  • AI Engineering/ Framework: Strong hands‑on experience with Python building ML/DL with libraries (e.g. TensorFlow, PyTorch, Keras, Scikit-learn), and LLM‑based systems and agentic frameworks including RAG architectures, prompt engineering, embeddings, fine‑tuning, evaluation and orchestration (e.g. ADK, LangChain, LangGraph, Vertex AI, Claude).
  • Software & Data Engineering/ Framework: Experience with Java, JavaScript/TypeScript, React, FastAPI, SQL/PostgreSQL, Snowflake, S3, and enterprise data and knowledge systems (e.g. BigQuery, Neo4j).
  • Cloud, DevOps & MLOps: Proficient with Git, Docker, CI/CD, and cloud platforms (AWS/GCP/Azure), with a strong focus on reproducibility, deployment, monitoring, and production‑ready MLOps.

Nice to have

  • Experience in life sciences, pharma, biotech, systems biology, immunology, translational science, omics, or related research environments.
  • Experience operating across scientific and technical disciplines, with enough domain fluency to engage credibly with scientists while still bringing a strong applied-AI builder mindset.

What the JD emphasized

  • production-grade AI systems
  • regulated environment
  • LLMs and agentic AI
  • prototype to production
  • MLOps best practices
  • AI agentic solutions
  • production-ready ML and analytics pipelines
  • data integrity standards
  • governance best practices
  • model validation
  • versioning
  • monitoring
  • reproducibility
  • scalability
  • deployment
  • reliability
  • security
  • integration
  • AI engineering system lifecycle

Other signals

  • design, build, and deploy production-grade AI systems
  • practical application of LLMs and agentic AI
  • take solutions from prototype to production
  • embedding MLOps best practices
  • AI agentic solutions that can plan and execute multi-step workflows