Data & MLOps Engineer (Mid-level) m/f

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.

HERE'S WHAT YOU'LL NEED:

• 3–5 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.