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MLOps / ML Engineer - Natural Negative
ML Engineer
Lead end‑to‑end ML model development and deployment, building scalable MLOps pipelines on AWS, Docker, and Kubernetes to accelerate biomaterials research and production at scale.
About the role
Key Responsibilities
- Design, develop, and production‑grade deploy ML models for biomaterials optimization using Python and modern ML frameworks.
- Build and maintain end‑to‑end MLOps pipelines on AWS, Docker, and Kubernetes, ensuring reproducibility and scalability.
- Implement CI/CD workflows for model training, testing, and deployment, integrating automated monitoring and alerting.
- Collaborate with data scientists, materials scientists, and software engineers to translate research insights into production‑ready solutions.
- Optimize model performance and resource usage, applying techniques such as hyperparameter tuning, model compression, and inference acceleration.
Requirements
- Strong experience with Python, TensorFlow/PyTorch, and ML model deployment.
- Proven expertise in MLOps practices, including Docker, Kubernetes, and AWS services (SageMaker, ECS, EKS).
- Hands‑on knowledge of CI/CD pipelines (GitHub Actions, Jenkins, ArgoCD) and automated testing.
- Excellent problem‑solving skills and ability to work in a fast‑moving, interdisciplinary team.
- Experience in the life‑sciences or materials domain is a plus.
Skills
pythonmachine learningmlopsawsdockerkubernetescicd