Director, AI Engineering Tech Lead

Pfizer Pfizer · Pharma · Mumbai, India, India

Director, AI Engineering Tech Lead at Pfizer, responsible for end-to-end engineering delivery of AI/ML-powered products like DecisionIQ. This role involves leading a team to architect, develop, and deploy reliable AI solutions, focusing on production-grade features, model integration, inference pipelines, and data transformations. The role also emphasizes engineering excellence, CI/CD, observability, DevSecOps, and people leadership within a regulated healthcare environment.

What you'd actually do

  1. Own the end-to-end engineering delivery of DecisionIQ: from user story refinement and technical design through development, testing, and production release
  2. Drive product vision alignment by partnering with Product Owners and commercial stakeholders to ensure every feature delivers measurable user value
  3. Lead development of AI/ML-powered features — model integration, inference pipelines, data transformations — with a relentless focus on user experience and adoption
  4. Ensure production-ready models across the lifecycle including training, monitoring, retraining, and rollback
  5. Establish and maintain CI/CD pipelines, automated testing, and deployment standards that ensure reliable, frequent releases

Skills

Required

  • Python
  • ML frameworks (scikit-learn, TensorFlow, PyTorch)
  • cloud platforms (AWS or Azure)
  • containerization (Docker/Kubernetes)
  • CI/CD (GitHub Actions)
  • LLMOps frameworks (LangChain, MLflow, Langfuse)
  • cloud AI services (SageMaker, Bedrock, Azure AI Foundry)
  • building and shipping AI/ML-powered products end-to-end
  • leading technical teams with people management responsibilities

Nice to have

  • Advanced degree (MS/PhD)
  • full-stack web development (React, Vue; HTML, Tailwind CSS, Bootstrap)
  • data science platforms (Dataiku DSS, SageMaker)
  • BI/visualization tools (Tableau, Power BI, Dash, Streamlit)
  • product management or product-led engineering teams
  • Certifications: AWS/Azure Professional, CKA/CKAD, ISTQB, HashiCorp Terraform

What the JD emphasized

  • end-to-end engineering delivery
  • AI/ML-powered features
  • production-ready models
  • LLMOps frameworks
  • cloud AI services
  • regulated/compliance-aware environments

Other signals

  • AI Engineering Product Delivery
  • end-to-end engineering delivery
  • AI/ML-powered features
  • production-ready models
  • accelerate feature delivery
  • integrate analytical models
  • LLMOps frameworks
  • cloud AI services