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Senior MLOps Engineer - Boehringer Ingelheim
MLOps Engineer
Lead the design, deployment, and scaling of production‑grade AI models for disease research, using Python, Docker, Kubernetes, CI/CD pipelines, and AWS cloud services.
About the role
Key Responsibilities
- Architect, build, and maintain end‑to‑end MLOps pipelines that move AI models from research to production at scale.
- Collaborate with data scientists and computational biologists to containerize models using Docker and orchestrate workloads on Kubernetes.
- Implement robust CI/CD workflows, automated testing, and monitoring to ensure model reliability and reproducibility.
- Manage cloud infrastructure on AWS, including EC2, S3, SageMaker, and IAM, optimizing cost and performance.
- Establish best practices for model versioning, metadata tracking, and experiment management with tools such as MLflow.
- Provide technical mentorship and guidance to junior engineers and cross‑functional teams.
Requirements
- 5+ years of hands‑on experience in MLOps or DevOps roles, with a strong focus on AI/ML workloads.
- Proficiency in Python and container technologies (Docker, Kubernetes) for model packaging and deployment.
- Deep understanding of CI/CD concepts and tools (GitLab CI, Jenkins, GitHub Actions) and experience building automated pipelines.
- Extensive experience with AWS services and infrastructure‑as‑code (Terraform, CloudFormation).
- Familiarity with ML frameworks such as TensorFlow or PyTorch and experiment‑tracking platforms like MLflow.
Skills
pythondockerkubernetescicdawstensorflow