Generative AI Engineer

We are Team possible – the people behind Proximus, Proximus NXT, Davinsi Labs, Codit, Proximus Ada, and more. Nice to see you here!

United by a shared purpose, we’re building a smarter, trustful and more connected world.

That means embracing technology and celebrating change.

We think possible and then make it possible. And of course, we love what we do.

Sounds like your kind of place?

Your job

Are you a passionate machine learning engineer with expertise in generative AI? Join our Machine Learning Enablers team at Proximus Ada, where you’ll play a key role in advancing and scaling generative AI capabilities across teams. You will leverage your expertise in architectures such as retrieval-augmented generation (RAG) and agent-based systems to develop and maintain reusable components and templates that enable data scientists to deliver impactful solutions.

In this role, you will collaborate closely with data scientists in delivery teams and engineers from our Cloud and DevSecOps teams to implement best practices and ensure technical excellence across multiple projects. Using frameworks such as LangChain and LangGraph and our Azure-first stack, you will maintain and expand a shared repository of reusable generative AI assets that enable scalable, reliable solutions.

Your innovative mindset will help identify emerging techniques and translate them into practical building blocks that deliver business value, keeping our teams aligned with the latest advances. Your work will support the day-to-day needs of our data scientists through the practical maintenance , hands‑on support , and enhancement of shared assets, while also driving innovation in our generative AI initiatives.

Responsibilities

Develop and Maintain our Generative AI Repository

• Manage and expand our shared repository of reusable Generative AI components and templates, ensuring it is robust, up-to-date, well-documented, and easy to adopt across use cases.
• Support onboarding and adoption: help teams use the repository effectively, keep alignment with the main branch, and facilitate clean integration of shared changes.
• Collaborate with data scientists to identify new components to build, provide technical support, and promote best practices in using the repository.
• Drive key upgrades and migrations of core libraries and templates (e.g., LangChain/LangGraph) with minimal disruption to delivery teams.

Enable Agent-Based and Generative AI Solutions

• Guide delivery teams on architectures such as retrieval-augmented generation (RAG) and agent-based systems, providing hands‑on technical support and troubleshooting when needed.
• Research and prototype emerging techniques, frameworks, and Azure services; translate validated approaches into reusable building blocks for delivery teams.

Collaborate and Drive Technical Excellence

• Define and promote software engineering best practices for Generative AI solutions (testing, code quality, style, automation) and enforce them through PR reviews and shared standards.
• Collaborate with Cloud, DevSecOps, enterprise architecture, and vendors to ensure solutions and technologies align with our stack and constraints.
• Stay current with advances in Generative AI and communicate relevant learnings and recommendations to the organization.