remote
MLOps/ ML Engineer
ML Engineer
MLOps/ML Engineer responsible for building and scaling AI pipelines that accelerate the development of compostable biomaterials, leveraging Python, TensorFlow, Docker, Kubernetes and cloud services.
About the role
Key Responsibilities
- Design, develop, and maintain end‑to‑end machine‑learning pipelines for biomaterial formulation optimization.
- Containerize models and services using Docker and orchestrate them on Kubernetes clusters.
- Implement CI/CD workflows to automate model training, testing, and deployment on AWS.
- Collaborate with data scientists and material scientists to translate research prototypes into production‑ready solutions.
- Monitor model performance in production, troubleshoot issues, and iterate to improve accuracy and efficiency.
Requirements
- Strong proficiency in Python and experience with deep‑learning frameworks such as TensorFlow or PyTorch.
- Hands‑on experience with Docker, Kubernetes, and cloud platforms (AWS preferred) for scalable deployments.
- Knowledge of CI/CD tools (e.g., GitHub Actions, Jenkins) and infrastructure‑as‑code concepts.
- Understanding of MLOps best practices, including model versioning, monitoring, and reproducibility.
- Ability to work cross‑functionally with scientific teams and communicate technical concepts clearly.
Skills
pythontensorflowdockerkubernetesawscicd