Senior Machine Learning Engineer

Compare the Market; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading financial services provider in Greater London is looking for a Senior Machine Learning Engineer to enhance AI capabilities. The role involves delivering production-ready ML solutions, designing robust model pipelines, and monitoring performance across models. Candidates should have strong Python skills, experience with cloud-native deployments, and familiarity with modern ML tools. You'll collaborate with data scientists and contribute to best practices in ML development, shaping a high-performance functionality within the organization.

Qualifications

• Experience in cloud-native ML model deployment.
• Skills in Python and another language like Go or Java.
• Ability to build and maintain ML pipelines and APIs.

Responsibilities

• Deliver end-to-end production ML solutions.
• Design model pipelines using modern tools.
• Monitor model performance and operational health.

Skills

Deploying ML models into production

Software engineering in Python

Modern ML tooling (MLflow, TFX, Airflow)

CI/CD pipelines and infrastructure-as-code

Collaboration and communication skills

Tools

Kubernetes

Databricks

SageMaker

Job description

At Compare the Market, we're scaling our AI capabilities to power intelligent, personalised experiences that help millions make smarter financial decisions. As a Senior Machine Learning Engineer, you'll play a critical role in enabling the deployment, monitoring, and scaling of production-grade ML systems – ensuring that our AI ambitions are not only possible but production-ready. This role blends hands‑on engineering with architectural design, experimentation support, and MLOps best practices. You'll work closely with data scientists, platform engineers, and product teams to build the infrastructure and tooling that powers our most advanced models. You'll also contribute to technical standards, advocate for scalable and responsible ML development, and help shape a high‑performance ML Engineering function.

What You’ll Be Doing – ML Engineering & Deployment

• Own the end‑to‑end delivery of production ML solutions in collaboration with data scientists and product teams
• Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes, Airflow)
• Contribute hands‑on code to model packaging, deployment, and lifecycle automation
• Build systems that monitor model performance, drift, and operational health in real time
• Support both batch and real‑time ML workloads depending on use case requirements

Platform & Standards

• Define and promote best practices for reproducibility, testing, CI/CD, and model observability
• Help evolve our internal ML platform to support experimentation, governance, and collaboration
• Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development

Collaboration & Technical Leadership

• Work closely with data scientists to productionise experimental models and turn prototypes into robust services
• Act as a technical mentor and code reviewer for other engineers and contributors
• Provide architectural guidance across multiple ML projects and technical design sessions

Culture & Innovation

• Contribute to a culture of engineering excellence, collaboration, and learning
• Stay up to date on emerging tools and approaches in MLOps and applied AI
• Support responsible AI practices by contributing to explainability, auditability, and fairness initiatives in ML systems

Qualifications

• Strong experience deploying ML models into production in cloud‑native environments
• Solid software engineering skills in Python (and optionally one other language, such as Go or Java)
• Experience with modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI)
• Familiarity with CI/CD pipelines and infrastructure‑as‑code (e.g. Terraform, CloudFormation, GitHub Actions)
• Experience building robust, maintainable, and testable ML pipelines and APIs, including batch or real‑time model delivery
• Strong understanding of ML lifecycle challenges – versioning, testing, monitoring, governance
• Excellent collaboration and communication skills; able to work across disciplines

Nice to Have

• Experience working in regulated sectors such as insurance, banking, or financial services
• Familiarity with platforms such as Databricks, SageMaker, Vertex AI, or Kubeflow
• Experience deploying real‑time or streaming ML models (e.g. Kafka, Flink, Spark Structured Streaming)
• Exposure to large language models (LLMs), vector databases, or RAG architectures
• Passion for automation, tooling, and building reusable systems
• Interest in responsible AI and ML model governance