onsite
ML Ops Engineer - Moonpig
MLOps Engineer
ML Ops Engineer building scalable, production‑grade ML pipelines on AWS using Docker, Kubernetes, and CI/CD, while collaborating with data scientists to deploy TensorFlow models and monitor performance.
About the role
Key Responsibilities
- Design, implement, and maintain end‑to‑end ML pipelines from data ingestion to model serving on AWS.
- Containerise models with Docker and orchestrate deployments using Kubernetes.
- Integrate CI/CD workflows (GitHub Actions, Jenkins) for automated testing, packaging, and rollout.
- Collaborate with data science teams to optimise model performance and ensure reproducibility.
- Monitor model health, latency, and resource utilisation; implement alerting and auto‑scaling.
- Document best practices, architecture decisions, and troubleshooting guides.
Requirements
- 3+ years of experience in ML Ops or related roles.
- Proficient in Python, Docker, Kubernetes, and AWS services (EKS, S3, SageMaker).
- Hands‑on experience with CI/CD pipelines and automated testing frameworks.
- Strong understanding of ML frameworks such as TensorFlow or PyTorch.
- Excellent problem‑solving skills and a collaborative mindset.
Skills
pythondockerkubernetesawscicdtensorflowmachine learning