Director, Enterprise AI Platforms

Director, Enterprise AI Platforms

Introduction to role

Are you ready to architect and scale an enterprise AI platform that accelerates how new medicines reach patients? Do you want to turn modern data and AI capabilities into tangible outcomes for scientists, commercial teams, and operations across a global enterprise? This is a rare chance to set the direction, build the golden paths, and deliver the secure, scalable foundation that moves ideas from lab notebooks to production at pace.

In this role, you will own the strategy and execution of our AI platform end to end—from experimentation and training to serving and observability—so teams can innovate quickly and safely. You will partner across architecture, product management, data, cyber security, and business leaders to align platform decisions to measurable impact. Your work will reduce friction, industrialize guidelines, and unlock the full power of AI at scale.

Accountabilities

- Target Architecture and Roadmap: Define the target architecture and multi-year roadmap covering experimentation, training, feature management, model registry, CI/CD, serving, and observability. Ensure multi-tenant, multi-region, and high availability designs with clear guardrails.
- Product and Portfolio Leadership: Partner with product management to shape platform vision, backlogs, and OKRs. Establish golden paths, templates, and self-service experiences that reduce friction from ideation to industrialization.
- Performance and Cost Optimization: Own capacity planning and cost optimization for GPU/CPU workloads. Drive performance engineering for distributed training and inference and set standards for scalability and efficiency.
- Data and Pipeline Integration: Integrate with enterprise data platforms and orchestrators to enable scalable pipelines, reproducible experiments, and governed access to datasets.
- Security and Compliance by Design: Define identity and secrets management, encryption, and vulnerability management approaches. Partner with Cyber Security and Data Privacy to meet GxP and internal standards without hindering productivity.
- Reusable Services and APIs: Drive reusable platform components, common services, and APIs that support multiple business units and improve leverage across the enterprise.
- Engineering Excellence and Coaching: Coach engineers and data scientists, set engineering standards, review designs, and lead architecture forums. Ensure alignment with enterprise guardrails and standard methodologies.
- Executive Communication and Alignment: Translate complex platform concepts for senior partners and align solutions to priority business outcomes across R&D, Commercial, and Operations.
- Multi-functional Leadership: Lead multi-functional teams and align platform choices with business and compliance priorities while fostering a culture of DevOps and continuous improvement.

Essential Skills/Experience

- Strong analytical and problem-solving skills to address challenges.
- Proven and creative technical leadership skills to drive detailed design and fact-based decision-making.
- Strong ability to create and communicate designs to engineers that are scalable and efficient AI platforms; implement and maintain the infrastructure and platforms that support the development and deployment of AI solutions.
- Experience in DevOps/MLOps/AIOps practices to streamline the development and deployment processes.
- Strong programming skills in Infrastructure as Code (e.g., Terraform, CloudFormation), AWS Services, collaborative software development, programming languages used in AI such as Python, proficiency in containerization technologies like Docker, etc.; and the ability to write clean, efficient, and maintainable code.
- Familiarity with big data technologies, including Apache Spark, for processing and analyzing large datasets.
- Understanding of security standard processes in AI systems and consistency to compliance standards.
- Willingness to stay updated with the latest advancements in AI technologies through continuous learning and professional development.
- Actively contributes to the continuous improvements/roadmaps of existing AI platforms.
- Lead multi-functional teams; align platform decisions with business and compliance priorities.
- Mentor and grow engineers; foster a culture of DevOps and continuous improvement.
- Elevate junior and mid level engineers through design reviews, pair architecture, and hands on guidance.

Desirable Skills/Experience

- Experience designing multi-tenant, multi-region, highly available AI/ML platforms in regulated environments (including GxP).
- Deep expertise in distributed training and inference (e.g., Kubernetes, Kubeflow, Ray, PyTorch Distributed, TensorRT/Triton).