AI Engineer

At CluePoints, we’re redefining how clinical trials are run. As the premier provider of Risk-Based Quality Management (RBQM) and Data Quality Oversight software, we harness advanced statistics, artificial intelligence, and machine learning to ensure the quality, accuracy, and integrity of clinical trial data, helping life sciences organisations bring safer, more effective treatments to patients faster.

We’re an ambitious, fast-growing technology scale-up with a dynamic and diverse international team. Collaboration, flexibility, and continuous learning are part of our DNA, and we are guided by our values of Care, Passion, and Smart Disruption.

The Role

The AI Engineer will build the AI capabilities that sit on top of CluePoints’ new data layer: the platform that harmonizes data across clinical studies and applications and makes it available to CluePoints’ consumer applications. You will design, evaluate and ship AI solutions, such as metadata-driven variable harmonization and retrieval and knowledge-graph services, that are auditable, traceable and fit for a regulated clinical-trial environment.
CluePoints has committed to building this data layer, so the role is an opportunity to shape the AI approach from the start. Success looks like AI components that measurably accelerate harmonization and whose outputs can be explained and defended in front of an auditor.

• Demonstrated experience building and shipping AI solutions into production, not only prototypes.
• Hands-on experience with RAG, including retrieval design, grounding and evaluation of results.
• Working knowledge of knowledge graphs, including when they are and are not the right tool.
• Strong Python skills and solid software-engineering fundamentals (testing, code review, API design).
• Experience evaluating AI outputs systematically, using test sets, accuracy measures and regression checks.
• Understanding of regulatory compliance, auditability and traceability in platform and system design.
• Comfort evolving quickly and experimenting with different technology capabilities, choosing solutions on evidence.
• Ability to work with data from multiple sources whose structures and variables differ.
• Ability to explain complex technical concepts clearly to technical and non-technical stakeholders.

Nice To Haves

• Experience in clinical trials, life sciences or another regulated environment. Strong candidates from other sectors, such as fintech, will be considered.
• Familiarity with Databricks and/or Snowflake and with Microsoft Azure.
• Familiarity with spec-driven development frameworks such as BMAD, Superpowers or Spec Kit.
• Exposure to clinical data standards (for example CDISC) or to metadata from electronic data-capture systems.
• Experience building structured AI workflows, such as prompt pipelines, agent orchestration or automated review.

AI Solution Design & Delivery

• Design, build and ship AI solutions that support the data layer, including approaches that use metadata from data-capture solutions to help harmonize differing study variables.
• Evaluate and experiment with AI and retrieval options (LLMs, RAG, knowledge graphs) and recommend the approach best suited to each problem rather than defaulting to the newest tool.
• Prototype quickly against realistic study data, then take successful prototypes through to production-quality services.

Retrieval & Knowledge Graphs

• Design and implement RAG pipelines over study metadata, data dictionaries and variable definitions.
• Apply knowledge-graph approaches to model relationships between studies, variables and concepts where they add value over conventional structures.
• Expose AI and retrieval capabilities through well-defined interfaces that CluePoints’ consumer applications can use.

Evaluation, Auditability & Compliance

• Define evaluation methods and quality thresholds for AI outputs, such as proposed variable mappings, and measure them against reviewed examples.
• Make AI-supported outputs traceable and auditable, with versioned prompts, models and configurations and a clear record of what was generated and what a person approved.
• Build proportionate human-review controls into AI-assisted harmonization workflows.
• Work with the architect to ensure AI components meet regulatory compliance expectations within the platform design.

Collaboration & Engineering Practice

• Work with the architect and data engineer on how AI components fit the data-layer architecture, harmonized snapshots and connectors.
• Clarify the underlying problem and expected value with product stakeholders before building.
• Write production-grade, tested and documented code, using AI-assisted and spec-driven development practices where they improve quality and speed.
• Document design decisions and share knowledge so the capability does not depend on one person.