Data & MLOps Engineer (Mid-level) mf

ARHS Group part of Accenture is looking for a Data & MLOps Engineer (Mid-level) to join our internal team in Luxembourg.

In this role you will design build and operate production-grade data and machine learning pipelines. Working closely with data scientists software engineers and cloud specialists you will help transform analytical and ML prototypes into secure scalable and production-ready services while contributing across the full data and MLOps lifecycle.

THE WORK:

• Build and maintain data pipelines for ingestion validation transformation and delivery of structured and unstructured data.
• Industrialize machine learning workloads by packaging models automating deployments and ensuring reproducible environments.
• Implement MLOps practices including experiment tracking model versioning model registries automated testing approval workflows and rollback strategies.
• Develop and operate APIs and batch services exposing data products and machine learning capabilities.
• Build and maintain CI/CD pipelines for data and ML solutions.
• Deploy containerized workloads using Docker Kubernetes and managed cloud services.
• Implement observability through monitoring logging metrics data quality checks drift detection and alerting.
• Collaborate with data scientists and business stakeholders to deliver production-ready solutions.
• Contribute to infrastructure automation using Infrastructure as Code tools such as Terraform and Ansible.
• Produce technical documentation and contribute to knowledge sharing across engineering teams.

Our roles require in-person time to encourage collaboration learning and relationship-building with clients colleagues and communities. As an employer we will be as flexible as possible to support your specific work/life needs.

HERES WHAT YOULL NEED:

• 35 years of experience in Data Engineering Software Engineering Cloud Engineering or MLOps.
• Strong programming skills in Python and SQL with a solid understanding of REST APIs and software engineering principles.
• Experience designing and operating ETL/ELT pipelines using tools such as Airflow Azure Data Factory AWS Glue Databricks dbt or equivalent.
• Hands-on experience with MLOps concepts including model packaging model serving experiment tracking and lifecycle management.
• Experience with Azure and/or AWS cloud platforms.
• Good knowledge of Docker; experience with Kubernetes and Helm is a strong asset.
• Experience with Git-based CI/CD Infrastructure as Code Terraform and/or Ansible.
• Familiarity with relational databases and object storage; exposure to Kafka NoSQL databases or event-driven architectures is a plus.
• Experience with monitoring logging troubleshooting and operational best practices.
• Understanding of secure software engineering and data governance principles.
• Fluency in French is mandatory with a good command of English.

BONUS POINTS IF YOU HAVE:

• Knowledge of machine learning fundamentals and model lifecycle management.
• Experience with MLflow Kubeflow or similar MLOps platforms.
• Exposure to Generative AI vector search embeddings RAG pipelines or LLM serving.
• Experience with Spark distributed processing or lakehouse architectures.
• Azure AWS Kubernetes Databricks or Terraform certifications.

Remote Work :

No

Employment Type :

Full-time