Machine Learning Engineer
On-siteLondon, England, United Kingdom or Berlin, State of Berlin, Germany
Job Summary
Design and maintain scalable MLOps infrastructure for machine learning and generative AI applications, including automated training, validation, testing, and CI/CD pipelines. Implement experiment tracking, model versioning, and artifact management while building workflow orchestration and feature engineering pipelines. Monitor production systems for performance, data quality, and drift, managing the end-to-end model lifecycle from retraining to governance. Containerize workloads with Docker and deploy services using cloud-native technologies and Infrastructure as Code. Collaborate with Data Scientists to productionize and scale AI solutions within the financial services domain.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related field
- Strong Python programming skills and proficiency with SQL
- Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management
- Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker
- Strong understanding of the end-to-end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance
- Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation)
- Experience with Docker, containerized ML workloads, and container orchestration platforms such as Kubernetes
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability
- Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices
Desired Qualifications
- Experience deploying LLM or Generative AI applications
- Excellent problem-solving skills
- Excellent communication skills
- Excellent collaboration skills
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