Machine Learning Engineer

We’re hiring an MLOps Engineer to create and maintain the systems that keep advanced AI models running smoothly at scale. In this role, you’ll work alongside data science and engineering teams to design production-ready pipelines, automate deployments, and ensure models remain performant and reliable in real-world conditions.

You’ll play a hands-on role in shaping the operational backbone of large language model (LLM)–powered features, building the frameworks and tooling needed to deliver them to users with confidence.

Key Impact Areas

• Build automated workflows for model training, evaluation, deployment, and monitoring.
• Establish best practices for CI/CD in machine learning, including model testing and version control.
• Monitor and track model behaviour in production, detecting drift and ensuring data quality.
• Partner with Data Scientists to containerise and operationalise models, including those with advanced retrieval or graph-based components.
• Configure and optimise cloud-based ML infrastructure (AWS, GCP, or Azure) for performance and scalability.
• Apply Infrastructure as Code (Terraform, Pulumi, etc.) to provision and manage environments.
• Integrate AI services into broader application architectures, working closely with product and engineering.
• Drive continuous improvement across the ML lifecycle, from A/B testing to incident response.

What You’ll Bring

• 3+ years in MLOps, DevOps, or software engineering focused on ML systems.
• Strong programming skills in Python; exposure to JavaScript/TypeScript a plus.
• Familiarity with LLM deployment and operational support.
• Experience with Docker, Kubernetes, and cloud ML services.
• Understanding of vector databases and search optimisation strategies.
• Hands-on knowledge of monitoring and observability tools (e.g., Prometheus, Grafana).