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# ML Engineer

- **Company:** The FA
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, Microsoft Azure, Cloud Computing, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Management of Software Versions, Data Logging, Pytorch, Microsoft Fabric, Containerization, Scikit Learn, Machine Learning Operations, Azure Synapse Analytics, Docker
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815448501-ml-engineer

## About the Role

* Strong Python engineering experience (testing, packaging, environment management).
 * Hands-on experience operationalising ML or advanced analytics in a cloud environment (e.g. Microsoft Fabric, Azure ML, Azure Synapse, or equivalent).
 * Practical experience with CI/CD pipelines (Azure DevOps or GitHub Actions).
 * Solid understanding of security, governance, data quality, and operational reliability in production systems.
 * Enough familiarity with common ML frameworks (e.g. scikit-learn, PyTorch, TensorFlow) to productionise models authored by others.

Beneficial to have:

 * Experience with model registries, feature management, or responsible AI tooling.
 * Exposure to monitoring and observability platforms for data/ML systems.
 * Knowledge of containerisation (Docker) or cloud orchestration patterns., * Collaborative and pragmatic, sharing knowledge within a team and ensuring best practices are followed.
 * Comfortable working in a matrix organisation with multiple partner teams.
 * Able to explain complex technical concepts clearly to non-engineering stakeholders.

## Description

+ Build, operate, and improve production-grade ML and analytics pipelines, ensuring they are reliable, reproducible, and well-governed.
 + Support the packaging, deployment, and lifecycle management of machine learning models developed across the organization.
 + Implement and maintain MLOps practices including versioning, automated testing, monitoring, logging, and controlled release processes.
 + Put in place model observability (performance tracking, drift monitoring, alerting) and work with delivery teams to agree appropriate measures.
 * Collaboration & Enablement
 + Act as a technical enabler for data scientists and analysts - providing patterns, tooling, and guardrails.
 + Translate analytical and business requirements into scalable, supportable platform components, aligned with FA architecture and governance standards.
 + Support the transition from prototype to production without redefining analytical ownership or methodological approach.
 * Engineering Standards & Reliability
 + Contribute to shared engineering standards, templates, and automation that reduce friction for analytics and data science teams.
 + Ensure ML services are secure, compliant, cost-aware, and auditable in line with FA policies.
 + Maintain clear technical documentation to support handover, continuity, and internal assurance.
 + Execute additional tasks as required to meet the FA's changing priorities.
 + Comply with all company policies and procedures to ensure that the highest standards of health, safety, and well-being can be maintained.


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