Company Description
Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development.
Arηs Group was acquired by Accenture in July 2024.
Job Description
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.
United States