ML Engineer

ML Engineer  

1. Role description 

 The primary goal is to design and implement the core REST API that powers the platform. Collaborate with other IT teams (e.g., security, cloud etc.) to deliver a reliable, secure and easy-to-use AI platform for teams to use. 

2. Skills 

Core skills include Python and asynchronous REST API development using FastAPI, relational database design, and infrastructure automation using Terraform. Strong Microsoft Azure experience is required, including identity and access management, secrets management and private networking, alongside Git, GitHub Actions, CI/CD pipelines, and automated testing. Strong troubleshooting, debugging, and error-handling skills, with experience implementing effective logging, monitoring, and observability practices, are also required. Familiarity with Azure Machine Learning, Microsoft Foundry, and MLOps practices is a plus. 

3. Desired background 

Demonstrated experience delivering production backend services, cloud platforms, or self-service infrastructure tooling, ideally supporting AI and machine learning teams. Strong hands-on experience with Microsoft Azure and Infrastructure as Code using Terraform is required; experience in energy, utilities, or other regulated enterprise environments is a plus. Able to own end-to-end feature delivery across APIs, databases, infrastructure, and deployment pipelines, while maintaining and improving an existing codebase. Experienced in collaborating with security, cloud, and networking teams to meet enterprise requirements. Produces maintainable, well-tested code, clear technical documentation and handovers, and can effectively communicate architecture decisions and technical trade-offs to both technical and non-technical stakeholders. 

4. Timeline 

5. Headcount 

One full-time ML Engineer, approximately 40 hours per week, subject to contract.