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# Machine Learning Engineer (MLOps focus)

- **Company:** Yechte Consulting
- **Location:** Belgium (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Big Data, Continuous Integration, Monitoring of Systems, Python (Programming Language), Machine Learning, SQL Databases, Management of Software Versions, Apache Spark, Reliability of Systems, Git, Scikit Learn, Kubernetes, Data Analytics, Machine Learning Operations, Docker
- **Published:** June 5, 2026
- **Apply:** https://be.indeed.com/viewjob?jk=c1f8e0e43ef564e7

## About the Role

Do you have experience in Spark?, Machine Learning Engineer (MLOps focus) with 6+ years of experience designing and automating end-to-end ML pipelines (CI/CD, CT), productionising models, and ensuring scalable, reliable deployments. Strong Python, SQL, Spark, Azure DevOps, and Git expertise with experience in model governance, monitoring, and cross-functional collaboration in data-driven environments., * Minimum 6 years of experience in ML Engineering or MLOps.

 * Strong proficiency in Python and SQL.
 * Experience with Apache Spark or similar frameworks for large-scale data processing.
 * Hands-on experience with CI/CD tools (e.g., Azure DevOps).
 * Familiarity with MLOps frameworks, model registries, and experiment tracking.
 * Proficient in Git for version control.
 * Ability to align technical deliverables with business objectives., * Experience with Docker, Kubernetes, and ML monitoring tools.
 * Solid understanding of ML algorithms (e.g., regression, classification, clustering).
 * Familiarity with libraries such as scikit-learn and pandas.
 * Ability to translate business requirements into technical solutions.
 * Strong customer focus and stakeholder engagement.

## Description

* Design and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.
 * Productionise ML models into clean, modular, and maintainable codebases.
 * Implement versioning and governance practices for data, code, and models.
 * Monitor model performance, data drift, and system reliability.
 * Collaborate with cross-functional teams to deliver scalable ML solutions.


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