Hybrid ML Engineer: Build Production AI Systems

Compare The Market; Greater London; Hybride; GBP 70 000 - 110 000; Plein temps

Résumé du poste

Compare the Market is seeking an ML-focused engineer to deliver end-to-end ML components in production environments and contribute to building scalable AI systems.

You will work with data scientists and engineers to productionise experimental models, monitor performance, and integrate LLM-powered capabilities into production systems. The role emphasises reproducibility, testing, CI/CD, and responsible AI practices.

Qualifications

• Some experience delivering ML or AI components in production or near-production environments.
• Strong Python software engineering skills and ability to write maintainable, testable code.
• Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent.
• Familiarity with CI/CD pipelines and how they apply to ML systems.
• Understanding of ML lifecycle challenges - versioning, testing, monitoring, governance.
• Good collaboration and communication across data science, engineering, and product teams.

Responsabilités

• Deliver well-scoped ML and AI system components end-to-end, from design through testing, deployment and production support.
• Contribute hands-on code to model packaging, deployment, and lifecycle automation.
• Build and improve systems that monitor model performance, drift, reliability, and operational health.
• Support both batch and real-time ML workloads depending on use case requirements.
• Work on emerging AI and LLM-powered capabilities and integrate modern AI techniques into production systems.

Connaissances

ML engineering

Python

CI/CD for ML

Databricks

MLflow

Airflow

Outils

Databricks

MLflow

Airflow

LangGraph

LangChain

Description du poste

Compare the Market is seeking an ML-focused engineer to deliver end-to-end ML components in production environments and contribute to building scalable AI systems.

You will work with data scientists and engineers to productionise experimental models, monitor performance, and integrate LLM-powered capabilities into production systems. The role emphasises reproducibility, testing, CI/CD, and responsible AI practices.