PpAre you a passionate machine learning engineer with expertise in generative AI? Join our bMachine Learning Enablers /b 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 bretrieval-augmented generation (RAG) /b and bagent-based systems /b to develop and maintain reusable components that enable data scientists to deliver impactful solutions — using bLangChain /b, bLangGraph /b, and our bAzure-first /b stack. /p h3Responsibilities /h3 ul liManage and expand our shared repository of reusable GenAI components and templates — keeping it robust, up-to-date, well-documented, and easy to adopt. /li liSupport onboarding and adoption: help teams use the repository effectively, maintain alignment with the main branch, and facilitate clean integration of shared changes. /li liCollaborate with data scientists to identify new components, provide technical support, and promote best practices. /li liDrive key library upgrades and migrations (e.g., LangChain / LangGraph) with minimal disruption to delivery teams. /li /ul h3Enable Agent-Based Generative AI Solutions /h3 ul liGuide delivery teams on RAG and agent-based architectures, providing hands‑on technical support and troubleshooting. /li liResearch and prototype emerging techniques, frameworks, and Azure services; translate validated approaches into reusable building blocks. /li /ul h3Collaborate Drive Technical Excellence /h3 ul liDefine and promote software engineering best practices for GenAI solutions (testing, code quality, automation) and enforce them through PR reviews and shared standards. /li liCollaborate with Cloud, DevSecOps, enterprise architecture, and vendors to ensure solutions align with our stack and constraints. /li liStay current with advances in Generative AI and communicate relevant learnings and recommendations to the organization. /li /ul h3Education /h3 ul liMaster's degree in AI, Computer Science, Software Engineering, Statistics, Mathematics, or a related quantitative field. /li liPh.D. is a plus, especially with research in Generative AI or agent-based systems. /li /ul h3Experience /h3 ul liMinimum 2+ years in AI/ML or software engineering in a business environment. /li liProven experience with generative AI models and LLMs in real‑world projects. /li liAbility to build reusable components and transition PoCs into production‑ready assets. /li liExperience providing technical guidance and support to delivery teams and stakeholders. /li /ul h3Technical Skills /h3 ul liStrong Python coding skills with solid software engineering practices (testing, code quality, documentation). /li liProficiency with Git and modern development workflows including CI/CD pipelines. /li liHands‑on experience with Microsoft Azure and relevant Azure Data AI services. /li liExperience with GenAI frameworks such as LangChain; familiarity with LangGraph is a plus. /li liMLOps best practices (e.g., experiment tracking with MLflow). /li liFamiliarity with monitoring and evaluation practices for Generative AI applications. /li /ul pPython LangChain LangGraph Azure AI MLflow CI/CD Git MLOps RAG /p h3Soft Skills /h3 ul liStrong problem‑solving and analytical skills with attention to detail. /li liClear communication: explaining technical concepts, actionable guidance, high‑quality documentation. /li liCollaboration mindset: supporting and mentoring through code reviews and hands‑on troubleshooting. /li liOwnership autonomy: prioritises effectively and drives work to completion in a transversal context. /li liCuriosity innovation: proactive in exploring new techniques and translating them into practical improvements. /li /ul h3Languages /h3 ul liEnglish — Fluent /li liFrench — Plus /li liDutch — Plus /li /ul /p #J-18808-Ljbffr