Description Join Euroclear Euroclear is a financial services company that specializes in the settlement of securities transactions, as well as the safekeeping and asset servicing of these securities. We are located in Brussels and several major cities in Europe and around the world. We are deeply convinced that diversity of talents, backgrounds and opinions is a key to success, by encouraging engagement, energy and innovation. The "Group Digital Capabilities" (GDC) division ensures Euroclear’s competitiveness by delivering reliable and sustainable IT solutions for the financial securities markets. Within GDC division, our AIR (Analytics, Insights and Reporting) tribe supports the needs for advanced analytics and reporting from all the entities of the Euroclear Group. Our scope can be summarized as follows: Operating and managing the evolution of the Data & Analytics Platform (DAP) consisting out of a DataLake, the Group Data Warehouse and a new off-premise Operational Data Store aligned with new market evolutions. Key player in the AI CoE. Centre of Expertise for BI & Reporting. Provide project support and expertise to various business lines. Help to Build Azure Cloud Data Platform for tomorrow Our AI Delivery squad helps to transform data into insights using techniques such as machine learning, natural language processing, large language models, mathematical optimization, etc. It is looking for an experienced ML Ops Engineer: Your profile Track record of hands-on experience in the area of AI / ML / Advanced Analytics, with focus on deploying and maintaining AI / ML models and services in production. Keywords: AI / ML application development, testing, serving, monitoring, troubleshooting. Ensuring ML models are reproducible and interpretable. Single-handedly packaged and deployed AI/ML services to production. Knowledge of monitor and maintain AI/ML services post-deployment. Software Engineering 5 years of work experience with Python, and AI/ML standard libraries such as pandas, scikit-learn, xgboost Nice-to-haves: Data processing libraries and frameworks (pydantic, pandera) Web frameworks (FastAPI, Flask, ) CLI frameworks (Typer, Click, ) General MLOps tools and frameworks (MLFlow, Azure ML Studio, ) Version control tools for ML datasets and models (DVC, Azure ML Dataset, ) ML monitoring libraries and solutions (NannyML, Evidently AI, ) Distributed processing libraries and frameworks (Ray, Dask, PySpark, ) Pipeline-building and orchestration libraries (such as Metaflow, ZenML, Kedro, Airflow, Dagster, ) General Python development tool (pytest, coverage, tox, mypy, ruff, uv, ) Familiarity with different programming paradigms (OOP, FP, ) Knowledge and application of good software design principles in daily practice. Accustomed to good testing practices (unit, integration, end-to-end, stress/performance). Writing clear, explicitly typed, documented code. Proven ability to turn a messy Jupyter notebook into a production-grade piece of code. Although we'll apply all possible preventive measure to prevent this from ever happening. Packaging python applications and libraries for distribution Proficient GIT usage, able to collaborate with multiple developers on a single repo. General Machine Learning/Data Science Good understanding of Machine Learning algorithms and their applications in NLP. Foundational Data Engineering skills (SQL, PySpark) Grasp of the modern ML Ops framework, ability to identify ML Ops maturity gaps and provide inputs for modernization efforts. Cloud Technologies 2 years of hands-on experience with deploying and maintaining applications on at least one major Cloud Platform Absolute preference for Azure Cloud. Focus on developing and serving ML applications (batch, streaming, synchronous). Familiarity with Unix/Linux command line tools and scripting (shell, bash): VIP club membership if you have at least once ran `rm -rf` on production data. Non-technical team player strong verbal and written communication skills as well as good customer relationship skills to present complex concepts and/or the results of a use case to different audiences (from end users up to division management). experience of working in large, complex enterprises and have stoically accepted it as your fate. not allergic to legacy technology and processes,yet are always on the lookout for modernization opportunities. up-to-date with new tools, technologies and approaches within the domain. able to estimate your short-term effort with reasonable accuracy and get the work done in the time frame you commit to. successfully swims in the waters of Agile project management techniques (scrum boards, standups, demos, reviews). able to promote ML Ops and advocate for its usage and necessity across the organization. Must love mentoring and sharing knowledge. Must love dad jokes. Your formal qualifications are the following: University degree in software engineering OR Data Science/Machine Learning/Data Engineering OR a related quantitative field, combined with strong IT skills. 5 years of experience with Python 2 years of experience of using DevOps/CI/CD practices. 2 years of experience in deploying AI solutions to production.