# Senior MLOps Engineer
Are you an MLOps Engineer who ensures machine learning doesn't get stuck in notebooks but runs reliably in production? adesso is looking for a Senior MLOps Engineer who combines technical expertise, oversight, and ownership.
## Your role from model to production
As a Senior MLOps Engineer, you lead initiatives where machine learning models are transformed into robust production solutions. You design and build ML architectures, pipelines, and CI/CD processes that support the full lifecycle: from training and deployment to monitoring and optimization.
You understand how cloud platforms, data infrastructure, and AI converge and know how to translate this into solutions that work in practice. With your technical depth and communication skills, you help clients move forward and contribute to the further development of our MLOps offerings.
## What you do
- Lead and deliver MLOps projects with a focus on scalability and reliability
- Design and implement ML architectures and pipelines for training, deployment, inference, and monitoring
- Establish and optimize CI/CD processes for machine learning
- Automate ML workflows on cloud and data platforms such as Azure, AWS, Databricks, Snowflake, and Dataiku
- Monitor and optimize model performance, costs, and stability in production
- Coach colleagues and share best practices
- Contribute to MLOps services, offerings, and sustainable client relationships
## What you bring
- Completed master's degree in Data Science, Computer Science, or equivalent
- Minimum 5 years of experience with MLOps, Data Engineering, or ML production environments
- In-depth knowledge of ML architectures, pipelines, and implementation strategies
- Experience with containerization and orchestration (Docker, Kubernetes)
- Experience with CI/CD tooling applied to ML (GitHub Actions, GitLab CI/CD, Azure DevOps, or Jenkins)
- Experience with monitoring and observability, including model drift and data quality
- Familiarity with orchestration tools such as MLflow, Kubeflow Pipelines, Airflow, or Prefect
- Knowledge of performance optimization such as distributed training, GPU scaling, and model compression
- Experience with cloud platforms (Azure, AWS) and ML services such as Azure ML or SageMaker
- Familiarity with data platforms such as Databricks, Snowflake, or Dataiku
- Experience with Infrastructure as Code (Terraform, ARM, or Bicep)
- Strong Python skills and experience with PyTorch or TensorFlow
- Basic knowledge of data engineering (ETL/ELT, streaming, Spark)
- Understanding of security, privacy, and compliance in AI applications
- Ability to explain complex technical decisions clearly to business and stakeholders
- Entrepreneurial mindset focused on quality, continuity, and customer value