Machine Learning Op's Engineer

Job Specification – MLOps Engineer Job Summary We are seeking an MLOps Engineer to design, implement, and manage the infrastructure, tooling, and processes that enable machine learning models to be deployed, monitored, and maintained in production. This role requires a hands-on engineer who bridges data science and operations, ensuring that AI solutions are reliable, scalable, and compliant with regulatory standards. You will be responsible for building CI/CD pipelines for models, monitoring their performance, and enabling continuous improvement through retraining and evaluation. You will collaborate closely with Machine Learning Engineers, Data Engineers, Product Leads, and Compliance specialists to ensure models deliver long-term value in real-world workflows. This position offers the opportunity to shape the operational backbone of AI adoption in the UK home-buying and mortgage market, enabling safe, efficient, and scalable use of AI in a highly regulated environment. Key Responsibilities Model Operations and Deployment Build and maintain CI/CD pipelines for training, testing, and deploying machine learning models. Automate workflows for model packaging, containerisation, and deployment. Ensure reproducibility, version control, and rollback mechanisms for models. Monitoring and Performance Management Implement monitoring systems for model accuracy, latency, drift, and bias. Set up alerting and logging to detect issues in production models. Collaborate with Data Scientists and ML Engineers to refine models based on monitoring insights. Infrastructure and Scalability Design and manage cloud-based infrastructure for model serving and data pipelines. Optimise infrastructure for cost efficiency and performance. Ensure systems are secure, resilient, and compliant with organisational policies. Collaboration and Delivery Work with Product and Delivery Managers to align operational readiness with business timelines. Collaborate with Solution Architects to ensure infrastructure fits within enterprise architecture. Partner with Compliance and Risk teams to embed auditability and explainability into pipelines. Continuous Improvement and Innovation Evaluate and introduce new tools and frameworks to improve MLOps practices. Drive automation across the AI lifecycle to accelerate delivery and reduce manual effort. Contribute to team best practices and knowledge sharing. Key Skills and Qualifications Strong skills in building CI/CD pipelines and automation frameworks. Proficiency in Python, shell scripting, and infrastructure-as-code (e.g., Terraform). Experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes). Familiarity with ML-specific tools (MLflow, Kubeflow, Vertex AI, SageMaker, or equivalent). Strong understanding of monitoring, observability, and logging tools. Knowledge of security, compliance, and data governance requirements. Qualifications and Experience 4+ years’ experience as an MLOps Engineer, DevOps Engineer, or related role. Proven track record of deploying and maintaining ML models in production. Experience in regulated industries (financial services, legal, PropTech) desirable. Familiarity with monitoring and managing LLMs or other advanced AI models an advantage. Degree in Computer Science, Engineering, or related field preferred. Job Type: Fixed term contractContract length: 6 months Pay: £450.00-£500.00 per day Work Location: In person