AI Engineer

Joining Collibra’s Unstructured AI Team

• Build Product Features: Work closely with engineering teams to build and deploy product features based on AI/ML models that retrieve and structure context for high-quality results.
• Support Technical Delivery: Contribute to the end-to-end delivery of Unstructured AI systems, moving features from prototype to stable production in enterprise environments.
• Develop Data Systems: Build full-stack systems to ingest and process unstructured content (PDFs, contracts, reports) from enterprise silos like SharePoint and Salesforce.
• Provide Technical Input: Participate in the creation and optimization of new microservices within the AI/ML landscape.
• Maintain Quality Standards: Contribute to code and ML quality standards, ensuring consistent tracking and high-performance building practices.
• Stay Current: Keep up-to-date with emerging technologies (e.g., Langchain, Keras, TensorFlow) and look for ways to adapt them within Collibra’s ecosystem.

This is a hybrid role based in our Brussels office. Our hybrid model means you’ll work from the office at least two days each week. This setup helps us stay connected, work more closely together, and keep making progress as a team.

Senior AI Engineer at Collibra are responsible for

• Adapt to Change: Understand that shifts in priorities impact task order and can pivot effectively between projects.
• Write Production Code: Are proficient in writing and reviewing production-grade backend code (Python, FastAPI).
• Collaborate on Design: Are eager to drive and support design discussions to enhance existing ML services.
• Deliver in Context: Have experience integrating diverse data sources to provide context for AI features.
• Communicate Clearly: Can explain technical concepts to both engineering peers and product stakeholders.
• Care About Data: Have a disciplined approach to data quality and ML model effectiveness.

You have

• Python Proficiency: Strong skills in data processing, API development, and integrations.
• AI/ML Tooling: Hands-on experience with modern frameworks like Langchain, Keras, or TensorFlow.
• Model Enrichment: Experience with LLM-based enrichment (classification, entity extraction, PII detection).
• Data Pipelines: Solid understanding of microservice architecture, API design, and Big Data frameworks (e.g., Spark).
• Enterprise Ingestion: Experience processing data from third-party sources (SaaS-based knowledge bases, OneDrive).

• A familiarity with metadata systems or data cataloging.
• Knowledge of model evaluation best practices and search relevance.
• A bachelor’s degree or equivalent related working experience is required.
• You must have work authorization to work in Belgium.

You are

• Calm, structured decision-making under tight timelines or ambiguity.
• Capable of communicating clearly across engineering, product, and field teams, ensuring alignment from prototype to rollout.
• Experienced in spotting risks early, course-correcting without friction, and model composure when delivery timelines are tight.
• Someone who cares deeply about data quality, precision, and governance.
• Strong communication and stakeholder-management skills across technical and business teams.

Measures of Success

• Within your first month, you will develop a deep understanding of the unstructured data stack and ship your first set of end-to-end features under the guidance of senior leadership.
• Within your third month, you will contribute significantly to the technical delivery of key product areas, ensuring stable processing for diverse document types.
• Within your sixth month, you will support the rollout of enterprise-grade AI features, helping to enhance existing ML services and sharing knowledge across the organization via internal sessions.