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CluePoints

Cloud-based RBQM platform for clinical trials

AI Engineer

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeBelgium

About the Job

Requirements

  • Demonstrated experience building and shipping AI solutions into production, not only prototypes.
  • Hands-on experience with retrieval-augmented generation (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, including testing, code review, and application programming interface (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 based 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.

Responsibilities

  • 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 large language models (LLMs), retrieval-augmented generation, and 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.
  • Design and implement retrieval-augmented generation 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 consumer applications can use.
  • 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.
  • 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.

Desired Qualifications

  • 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 such as CDISC or to metadata from electronic data-capture systems.
  • Experience building structured AI workflows, such as prompt pipelines, agent orchestration, or automated review.

About the Company

CluePoints

Cloud-based RBQM platform for clinical trials

CluePoints provides risk-based quality management and data quality oversight software for clinical trials. Its cloud-based platform analyzes trial data using advanced statistics, machine learning, and AI to identify risks and monitor data quality from initial risk assessment through issue tracking. Key products—Site Profile and Oversight Tool (SPOT), Intelligent Medical Coding (IMC), Intelligent Query Detection (IQD), and Medical & Safety Review (MSR)—automate data discrepancy detection, improve medical review efficiency, reduce manual coding, and enable adaptive site monitoring. The company differentiates itself with an end-to-end, multi-product platform (expanded in 2024) that combines RBQM, data analytics, and regulatory-aligned monitoring to help pharmaceutical/biotech companies and CROs de-risk trials and ensure data integrity and patient safety. Its goal is to help customers meet regulatory guidance from bodies like the FDA and EMA while improving trial quality and efficiency.

Company size
201-500
Company stage
Acquired
Total funding
$6.6M
Headquarters
Ottignies-Louvain-la-Neuve, Belgium
Founded
2012

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