About the Company
Machine Learning Reply provides consulting and engineering services for clients across industries, supporting cutting-edge projects in machine learning, data processing, and cloud infrastructure. The company partners with organizations to deliver data-intensive solutions, optimize workflows, and implement AI and ML-driven applications.
With a focus on collaboration, knowledge-sharing, and innovation, Machine Learning Reply enables teams to build robust, production-ready solutions while fostering professional growth in a dynamic, interdisciplinary environment.
About the Role
The DevOps / ML Engineer role involves designing, implementing, and maintaining data-intensive applications and machine learning solutions for clients, both in cloud-based and on-premises environments.
Key Responsibilities
- Design technical approaches for machine learning and AI applications
- Implement and take ownership of solutions on cloud platforms (AWS, Azure, GCP) or on-premises infrastructures
- Automate recurring tasks using DevOps and MLOps practices to reduce client time-to-delivery
- Ensure monitoring, failover, and recovery infrastructures comply with regulatory requirements
- Collaborate with clients and stakeholders to translate business requirements into production-ready solutions
- Work closely with enterprise architects, analysts, data scientists, and data engineers to develop data warehouses, data lakes, and data platforms
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Physics, or a related field
- Practical experience with DevOps / MLOps principles and cloud platforms (AWS, Azure, GCP, Databricks)
- Strong analytical and communication skills for presenting results to management
- Fluent English and German language skills (minimum B2 level)
Nice to Have
- Experience with cloud technologies (AWS, Azure, GCP), Kubernetes, and programming languages (Python, Java, Scala)
- Knowledge of SQL and NoSQL databases, data lakes
- Experience with Big Data technologies (Apache Spark), data streaming (Apache Kafka), and workflow orchestration tools (Apache Airflow, Dagster)
Benefits
- Exposure to projects across industries including Banking, Insurance, Automotive, Retail, and more
- Professional growth through interdisciplinary work and training in data engineering, cloud architecture, and data science
- Collaboration with industry-leading partners in cloud, BI, and AutoML
- Active social and professional programs including training, conferences, team-building, hackathons, and communities of practice
- Flexible work environment with hybrid options between client sites, company office, and remote work
- State-of-the-art equipment provided
- Award-winning office space in downtown Munich
- Public transport ticket with Deutschlandticket
- Gym membership subsidy