Role overview
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.
Enterprise Ingestion: Experience processing data from third-party sources (SaaS-based knowledge bases, OneDrive).
A familiarity with metadata systems or data cataloging.
What you'll work on
- 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.
What we're looking 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.
Tags & focus areas
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