checkatrade - MLOps Engineer

Moorgate, London, United Kingdom - Hybrid+ Equity2w ago

In Office EMEA Cloud Computing Artificial Intelligence ML Engineer MLOps AWS Airflow Vector Talent Acquisition

Requirements

• Streaming or event-driven systems for real-time inference. • LLM and agent infrastructure (MCP, retrieval-augmented generation, vector stores). • Feature stores, model registries and drift detection. • AWS experience, particularly managed Airflow (MWAA). • (Don't self-select out because you don't tick every box. We'd rather meet you.) • 💰 Competitive salary, annual bonus & equity scheme • 🩺 Health, wellbeing and learning support • 🚲 Gym membership, Smart Tech Scheme, Cycle Scheme • 🎂 Birthday/special day leave, buy & sell holiday scheme • ❤️ 1 week's paid charity leave, and more • Screening call with one of our Talent Acquisition Partners • Interview with the hiring manager and members of the team • Final stage with a senior stakeholder • Not this one? Stay close anyway. Join the Talent Community https://jobs.ashbyhq.com/checkatrade/form/register-interest and we'll ping you the second something better fits.

Responsibilities

• Deploy, automate, monitor and retrain ML models, moving refresh frequency towards real-time where it adds value. • Set the MLOps standards for the Data Science team: CI/CD, model versioning and reusable patterns that save everyone reinventing the wheel. • Build the infrastructure that lets AI tools and agents (including Claude, MCP services and knowledge bases) access Checkatrade data securely. • Help keep our GCP-based data platform (with some infrastructure in AWS) reliable and secure, and chip away at legacy and technical debt. • Watch models, pipelines, AI usage and platform services, so drift, reliability and cost issues get caught early. • Apply the right controls to ML and AI workloads, including sensitive-data access, lineage and reproducibility. • Strong Python, with proven experience deploying, serving and monitoring ML models in production. • Hands-on experience with GCP and its ML and data services. • Docker and Kubernetes, plus CI/CD and infrastructure-as-code. • Experience building observability for ML or data systems: metrics, alerting and cost monitoring. • A security-minded approach to data access and secrets management. • You take ownership of production systems and care about them long after launch, and you work well with Data Scientists and engineers alike. • You favour simple, maintainable solutions and raise the engineering bar around you.