Experienced ML Ops Engineer

Division: Group Digital Capabilities (GDC)

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 Euroclears 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 leading the evolution of the Data & Analytics Platform (DAP) consisting out of a Data Lake the Group Data Warehouse and a new offpremise 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 expert ML Ops Engineer:

Your profile

• Consistent track record of handson experience in the area of AI/ ML/ Advanced Analytics with special focus on deploying and maintaining AI/ ML models and services in production.
  • Keywords: AI/ ML application development testing serving monitoring problem solving.
  • Know how to ensure ML models are reproducible and interpretable.
  • Has already singlehandedly packaged and deployed AI/ML services to production!
  • Know how to supervise and maintain AI/ML services postdeployment.
• Proficient in Python!
  • 5 years of work experience with Python and AI/ML standard libraries such as pandas scikitlearn xgboost

Nicetohaves:

• Data processing libraries and frameworks (pydantic pandera)
• Web frameworks (such as 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 ...)
• Supervising libraries and solutions (such as NannyML Evidently AI ...)
• Distributed processing libraries and frameworks (such as Ray Dask PySpark ...)
• Pipelinebuilding and orchestration libraries (such as Metaflow ZenML Kedro Airflow Dagster ...)
• General Python development tool (pytest coverage tox mypy black ruff uv pipcompile ...)
• Can write both objectoriented and functional code and understand concepts such as (de)coupling coherence inheritance composition.
• Make sure the code that you and your colleagues write is thoroughly tested (unit integration endtoend stress/performance).
• Love and regularly use data validation and type hints.
• Know how to turn a messy jupyter notebook into a productiongrade piece of code; although well apply all possible preventive measure to prevent this from ever happening.
• Know how to package a python application or library for distribution
• Proficient GIT user able to collaborate with multiple developers on multiple repositories while following standard processes related to branching merging and code reviews.
• Good understanding of Machine Learning algorithms and their applications in NLP.
• Experience with at least one Cloud Provider preferably Azure Cloud.
• Experience 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.
• Possess the foundational Data Engineering skills allowing you to interact with the Data Engineering team and analyze and fix data pipelines if needed
  • You could handle using SQL to extract transform and load data (ETL/ELT).
  • Experience with the Hadoop ecosystem (Spark Kafka Hive Impala) is a plus.
    • Experience with the Cloudera distribution is an additional plus
• Understand the modern ML Ops framework and complexities it adds to DevOps.
  • Able to identify the ML Ops maturity gaps and provide inputs for modernization efforts.

Nontechnical

• 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 working in large sophisticated enterprises and have stoically accepted it as your fate.
• Not allergic to legacy technology yet are always on the lookout for modernization opportunities.
• Stay uptodate with new tools technologies and approaches within the domain.
• Wellintegrated standout colleague.
• Able to estimate your shortterm effort with reasonable accuracy and get the work done in the time frame you commit to.
• Efficiently swim in the waters of Agile project management techniques (scrum boards standups demos reviews).
• Stand to promote ML Ops and advocate for its usage and necessity across the organization.
• 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.

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