Director, AI Engineering--Clinical Development and Operations (CD&O)

POSITION SUMMARY

In this “hands-on” position, you will design, build, and deploy production-grade AI systems that will power enterprise-scale capabilities across the Clinical Development & Operations organization. This is a high-impact role for a builder who thrives on solving real-world business problems in a complex, data-rich, and regulated environment. You will be instrumental in advancing the practical application of LLMs and agentic AI by identifying high-value use cases, developing reusable workflows, and partnering with stakeholders to drive adoption and impact. Combining deep expertise in software engineering and machine learning, you will take solutions from prototype to production, embedding MLOps best practices to ensure they are reliable, scalable, and reproducible. You will drive business transformation through proactive thought-leadership, innovative analytical capabilities, and the ability to communicate highly complex information in new and creative ways.

WHAT YOU"LL DO

Develop and Implement AI Solutions

• 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.

• Contribute to the automation of manual and repetitive activities to improve speed, quality, and consistency.

Strengthen Operational Decision-Making

• Develop predictive, optimization, and scenario-based models to support clinical trial supply forecasting and operational planning.

• Create and maintain dashboards and decision-support tools that translate complex data into actionable insights for CD&O leadership and operational teams.

Engineer Production-Grade AI Systems

• Implement AI solutions that are aligned with data integrity standards and governance best practices, including model validation, versioning, and monitoring.

• Design and implement AI agentic solutions that can plan and execute multi-step workflows.

• Build robust, production-ready ML and analytics pipelines with a focus on reproducibility and scalability.

• Deploy AI solutions in cloud environments, ensuring reliability, security, and seamless integration with existing systems.

Collaborate Across Disciplines

• Partner closely with CD&O line teams, scientists, and Digital partners to ensure that AI efforts remain tightly aligned to real scientific needs and can be deployed in ways that are trusted, scalable, and adopted in day-to-day work.

• Champion best practices in AI engineering system lifecycle.

BASIC MINIMUM QUALIFICATIONS

• PhD in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 5 years of applied analytical experience with demonstrated impact in operations, automation, business analytics, or decision support OR

• Master’s in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 7 years of applied analytical experience with demonstrated impact in operations, automation, business analytics, or decision support.

• Strong hands-on experience applying LLMs, generative AI, machine learning, or related AI approaches to real-world workflows, products, or analytical use cases, ideally within R&D, clinical operations or large-scale regulated organizations.

• Experience building practical, reusable workflows or systems rather than one-off analyses, with strong implementation skills in Python and modern AI / ML tooling.

• Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled workflows.

• Experience working directly with domain users or stakeholders to translate ambiguous needs into useful technical solutions, with evidence of strong collaboration and communication skills.

TECHNICAL SKILLSET

• 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/Azur