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

POSITION SUMMARY

As a hands-on Senior Manager, AI Engineering, you will drive the technical execution, data engineering, and operationalization of production-grade AI systems within the Clinical Development & Operations (CD&O) organization.

In this individual-contributor engineering role, you will be focused on dependable delivery. Taking direction and technical guidance from the AI Platform Lead, you will work from defined business cases and requirements to deliver well-scoped AI/ML and LLM-based solutions to production. Your centre of gravity is data engineering, AI capability build, and operationalization - turning validated needs into reliable, scalable, and reproducible systems - with strong hands-on depth in a focused domain.

What you’ll do:

Analyze and Scope:

• Work from business cases and requirements defined, and translate them into clear technical requirements for your assigned use cases.
• Partner with operational and line teams to clarify scoped needs, edge cases, and acceptance criteria; surface risks and dependencies.

Engineer Data and Pipelines:

• Build and maintain robust, production-ready data pipelines within the established data and knowledge architecture within CD&O organisation.
• Ensure data quality, lineage, and reproducibility across the solutions you deliver.

Build and Deploy AI Solutions:

• Build, test, and deploy AI/ML and LLM-based solutions for assigned process-heavy workflows (e.g., protocol feasibility and site selection, study start-up), contributing hands-on to the majority of the engineering work.
• Implement agentic workflow components, tool integrations, prompts, evaluation routines, and orchestration patterns according to approved enterprise architecture and AI engineering standards.

Testing, Validation, and Production Support:

• Develop and execute unit testing, integration testing, model/LLM evaluation support, performance checks, and production-readiness activities to ensure delivered AI capabilities are robust and maintainable.

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.

MINIMUM QUALIFICATIONS

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline and 1+ years experience building practical, reusable workflows or systems OR
• Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline and 5+ years experience building practical, reusable workflows or systems OR
• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline and 6+ years experience building practical, reusable workflows or systems
• Strong implementation skills in Python and modern AI / ML tooling
• Strong hands-on experience with Python building ML/DL solutions 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, Databricks, Vertex AI, Claude).
• Experience with Java, JavaScript/TypeScript, React, FastAPI, SQL/PostgreSQL, Snowflake, S3, and enterprise data and knowledge systems.
• Proficiency with Git, Docker, CI/CD with a strong focus on reproducibility, deployment, monitoring, and production-ready MLOps.
• Experience working directly with domain users or stakeholders to translate ambiguous needs into useful technical solutions
• Strong collaboration and communication skills

PREFERRED QUALIFICATIONS

• 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.

Work Location Assignment: This is a hybrid role requiring you to live within commuting distance and work on-site an average of 2.5 days per week.

Candidate demonstrates a breadth of diverse le…