Machine Learning Engineer

Machine Learning Engineer – AWS Migration Outside IR35 | London Hybrid | 6 months+ We are seeking an experienced Machine Learning Engineer to assist in our client’s migration of their model training and deployment pipelines from an on-prem Kubernetes-based platform to AWS. This role is hands-on and will involve adapting existing workflows and tooling to AWS-native services, ensuring minimal disruption while optimising for performance and scalability. The ideal consultant will have a strong mix of ML engineering, Python development, and AWS expertise, with proven experience building production-grade ML pipelines in SageMaker. What you’ll be doing: • Migrate ML workflows (training, deployment, monitoring) from on-prem Kubernetes to AWS SageMaker. • Rewrite/refactor code to align with AWS-native services and best practices. • Build & optimise Python-based ML pipelines for scalable, production-ready deployment. • Collaborate with Data Science & DevOps teams to ensure a smooth transition. • Implement robust model monitoring, versioning, and CI/CD for ML. What we’re looking for: • Strong experience as a Machine Learning Engineer or ML-focused Software Engineer. • Proven track record building ML pipelines in AWS SageMaker. • Python development for ML automation & deployment. • Containerised ML workflows (Docker, Kubernetes). • Experience migrating ML systems from on-prem to cloud. Nice to have: • GPU-enabled Kubernetes cluster experience. • MLOps best-practice knowledge. • Familiarity with AWS services like Lambda, Step Functions, S3, ECR.