What are we looking for :

- 45 years of hands-on experience in software engineering, data engineering, or ML engineering, with meaningful exposure to production systems beyond notebooks or coursework.

- Solid end-to-end understanding of ML workflows data preparation, training, validation, deployment, and monitoring and the real-world challenges of keeping models reliable in production (latency, versioning, rollback, drift).

- Hands-on AWS experience, including core services (S3, SQS, Lambda) and ideally SageMaker and Bedrock; comfort with containerisation and infrastructure as-code.

- Strong Python skills, solid version control practices, and a working understanding of CI/CD concepts and production observability (logs, metrics, traces, dashboards).

- Full professional proficiency in English.

Key responsibilities in this role :

- Deploy, version, and manage ML model endpoints in production including LLM and computer vision pipelines ensuring reliability across multi-region AWS infrastructure.

- Build and maintain CI/CD pipelines for model delivery, incorporating automated testing, staged rollouts, and safe rollback procedures.

- Monitor model and system health : track performance metrics, detect drift, manage alerts, and lead incident response when things go wrong.

- Optimise cloud infrastructure costs and resource usage across SageMaker, Bedrock, Lambda, and related AWS services.

- Manage experiment tracking and model/data versioning, maintaining clean lineage across training, validation, and deployment stages.

- Write and maintain infrastructure-as-code to keep our environments reproducible, auditable, and scalable.