Are you a passionate machine learning engineer with expertise in generative AI? Join our
Are you the right candidate for this opportunity Make sure to read the full description below.
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
retrieval-augmented generation (RAG)
and
agent-based systems
to develop and maintain reusable components that enable data scientists to deliver impactful solutions — using
LangChain ,
LangGraph, and our
Azure-first
stack.
Responsibilities
Manage and expand our shared repository of reusable GenAI components and templates — keeping it robust, up-to-date, well-documented, and easy to adopt.
Support onboarding and adoption: help teams use the repository effectively, maintain alignment with the main branch, and facilitate clean integration of shared changes.
Collaborate with data scientists to identify new components, provide technical support, and promote best practices.
Drive key library upgrades and migrations (e.g., LangChain / LangGraph) with minimal disruption to delivery teams.
Enable Agent-Based & Generative AI Solutions
Guide delivery teams on RAG and agent-based architectures, providing hands‑on technical support and troubleshooting.
Research and prototype emerging techniques, frameworks, and Azure services; translate validated approaches into reusable building blocks.
Collaborate & Drive Technical Excellence
Define and promote software engineering best practices for GenAI solutions (testing, code quality, automation) and enforce them through PR reviews and shared standards.
Collaborate with Cloud, DevSecOps, enterprise architecture, and vendors to ensure solutions 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 AI, Computer Science, Software Engineering, Statistics, Mathematics, or a related quantitative field.
Ph.D. is a plus, especially with research in Generative AI or agent-based systems.
Experience
Minimum 2+ years in AI/ML or software engineering in a business environment.
Proven experience with generative AI models and LLMs in real‑world projects.
Ability to build reusable components and transition PoCs into production‑ready assets.
Experience providing technical guidance and support to delivery teams and stakeholders.
Technical Skills
Strong Python coding skills with solid software engineering practices (testing, code quality, documentation).
Proficiency with Git and modern development workflows including CI/CD pipelines.
Hands‑on experience with Microsoft Azure and relevant Azure Data & AI services.
Experience with GenAI frameworks such as LangChain; familiarity with LangGraph is a plus.
MLOps best practices (e.g., experiment tracking with MLflow).
Familiarity with monitoring and evaluation practices for Generative AI applications.
Python LangChain LangGraph Azure AI MLflow CI/CD Git MLOps RAG
Soft Skills
Strong problem‑solving and analytical skills with attention to detail.
Clear communication: explaining technical concepts, actionable guidance, high‑quality documentation.
Collaboration mindset: supporting and mentoring through code reviews and hands‑on troubleshooting.
Ownership & autonomy: prioritises effectively and drives work to completion in a transversal context. xphnsxz
Curiosity & innovation: proactive in exploring new techniques and translating them into practical improvements.
Languages
English — Fluent
French — Plus
Dutch — Plus
#J-18808-Ljbffr