remote
Machine Learning Associate Engineer - Eaton
ML Engineer
Develop and maintain scalable infrastructure for deploying machine learning models, collaborating with cross‑functional teams to deliver production‑ready AI solutions using Python, Docker, Kubernetes, and cloud services.
About the role
Key Responsibilities
- Design, build, and operate end‑to‑end MLOps pipelines for model training, validation, and deployment.
- Develop reusable infrastructure components using Docker, Kubernetes, and cloud services (AWS) to support scalable model serving.
- Implement CI/CD workflows and monitoring tools to ensure reliable, automated model releases.
- Collaborate with data scientists, product owners, and software engineers to translate business requirements into production‑ready ML solutions.
- Maintain documentation, version control, and best‑practice standards for reproducibility and governance.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, or related field with strong fundamentals in machine learning and software engineering.
- Proficiency in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, scikit‑learn).
- Hands‑on experience with containerization (Docker) and orchestration (Kubernetes) in cloud environments.
- Familiarity with CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and infrastructure‑as‑code concepts.
- Excellent problem‑solving skills and ability to work effectively in cross‑functional teams.
Skills
machine learningmlopspythondockerkubernetesawscicd