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.
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.
Education
Master’s degree in Artificial Intelligence, Computer Science, Software Engineering, Engineering, Statistics, Mathematics, or a related quantitative field.
A Ph.D. is a plus, especially with research in Generative AI or agent-based systems.
Professional Experience
Minimum of 2+ years of relevant experience in a business environment in AI/ML engineering or software engineering.
Proven experience working with generative AI models and LLMs in real-world projects.
Demonstrated ability to build reusable components and templates, and transition proof‑of‑concepts into production‑ready assets.
Experience collaborating with delivery teams and stakeholders, providing technical guidance and support.
Technical Skills
Strong coding skills in Python, with solid software engineering best practices (testing, code quality, documentation, maintainable design).
Proficiency with version control (Git) and modern development workflows, including CI/CD pipelines.
Hands‑on experience with Microsoft Azure and relevant Azure Data & AI services.
Experience with Generative AI frameworks such as LangChain; familiarity with LangGraph is a plus.
Experience implementing MLOps best practices (e.g., experiment tracking with MLflow).
Familiarity with monitoring and evaluation practices for Generative AI applications.
Soft Skills
Strong problem‑solving and analytical skills, with attention to detail.
Clear communication skills, including the ability to explain technical concepts, provide actionable guidance, and produce high‑quality documentation.
Collaboration and enablement mindset: comfortable supporting and mentoring others through code reviews and hands‑on troubleshooting.
Ownership and autonomy: able to prioritize effectively and drive work to completion in a transversal context.
Curiosity and innovation mindset: proactive in exploring new techniques and translating them into practical improvements.
Languages
Fluent in English and preferably also French and/or Dutch.
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