Machine Learning Engineer
Role details
Job location
Tech stack
Job description
- Develop and maintain machine learning pipelines for training, validation, and deployment
- Collaborate with data scientists to productionise models and turn prototypes into performant, reliable services
- Contribute to deployment tooling and automation for both batch and real-time ML use cases
- Build monitoring and alerting for model health, performance, and data drift
Platform & Standards
- Support the evolution of our internal ML platform and development workflows
- Apply best practices in testing, CI/CD, version control, and infrastructure-as-code
- Contribute to team libraries, reusable components, and shared deployment patterns
Collaboration & Growth
- Work in cross-functional teams alongside product managers, engineers, and analysts
- Participate in design sessions, peer reviews, and sprint planning
- Learn from and be mentored by experienced ML Engineers and technical leaders
Requirements
Do you have experience in Python?, We've carved a meerkat-shaped niche and we're looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you'll fit right in., Must Have
- Practical experience deploying ML models into production environments
- Strong Python development skills and understanding of ML model structures
- Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI
- Understanding of CI/CD concepts and basic infrastructure automation
- Ability to write well-tested, maintainable, and modular code
- Strong collaboration skills and a growth mindset
- A background in software engineering, computer science, or a quantitative field-or equivalent hands-on experience in ML delivery
Nice to Have
- Experience working in regulated sectors such as insurance, banking, or financial services
- Exposure to Databricks, container orchestration (e.g. Kubernetes), or workflow engines (e.g. Argo, Airflow)
- Familiarity with real-time model deployment, streaming data, or event-driven systems (e.g. Kafka, Flink)
- Interest in MLOps, model governance, and responsible AI practices
- Understanding of basic model evaluation, drift detection, and monitoring techniques
Why Compare the Market?
We're a business built for pace and performance. Here, you'll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
We believe diverse teams make better decisions, and we're committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
If you're ready to stretch yourself, raise the bar, and grow with a team that's serious about performance, innovation, and purpose, we'd love to hear from you.