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- Tasks: Design and deploy cutting-edge machine learning models in a fast-paced financial services environment.
- Company: Join a leading financial services organisation with a focus on innovation.
- Benefits: Competitive daily rate, remote work flexibility, and potential for contract extension.
- Why this job: Make a real impact by working on high-volume ML systems that drive business success.
- Qualifications: Proven experience in machine learning, strong Python skills, and familiarity with cloud platforms.
- Other info: Collaborative team environment with opportunities to enhance your skills in MLOps.
We are seeking an experienced Machine Learning Engineer to support a Financial Services organisation on an initial 6-month contract, working on production-grade ML systems that operate in regulated, high-volume environments.
This role is ideal for someone comfortable taking models from research through to deployment, with a strong appreciation for robust engineering, governance, and scalability.
Responsibilities- Design, build, and deploy machine learning models into production within a Financial Services environment
- Collaborate closely with Data Scientists, Software Engineers, Risk, and Product teams
- Build and maintain end-to-end ML pipelines (training, validation, inference, monitoring)
- Ensure models meet requirements around performance, resilience, and explainability
- Contribute to MLOps best practices, model governance, and technical standards
- Support model monitoring, drift detection, and ongoing optimisation
- Proven commercial experience as a Machine Learning Engineer, ideally within Financial Services, FinTech, or a regulated environment
- Strong Python skills and hands-on experience with ML libraries (TensorFlow, PyTorch, scikit-learn)
- Experience deploying and supporting ML models in production
- Solid understanding of data pipelines, versioning, testing, and software engineering best practices
- Experience working with cloud platforms (AWS, GCP, or Azure)
- Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.)
- Docker and Kubernetes experience
- Exposure to model governance, explainability, or regulatory frameworks
£650–£750 per day (Outside IR35)
Initial 6-month contract, with strong extension potential
Immediate or short-notice start preferred
Machine Learning Engineer in Oxford employer: Edison Smart
Contact Detail:
Edison Smart Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Oxford
✨Tip Number 1
Network like a pro! Reach out to your connections in the financial services sector and let them know you're on the lookout for a Machine Learning Engineer role. You never know who might have the inside scoop on an opportunity that’s not even advertised yet!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those relevant to financial services. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with ML libraries and cloud platforms, and don’t forget to highlight your understanding of model governance and explainability!
✨Tip Number 4
Apply through our website! We’ve got loads of opportunities waiting for talented Machine Learning Engineers like you. Plus, applying directly gives you a better chance of getting noticed by our hiring team!
We think you need these skills to ace Machine Learning Engineer in Oxford
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML models, especially in financial services, and don’t forget to showcase your Python skills and any relevant projects you've worked on.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for this role. Mention specific experiences that align with the job description, like your work with ML libraries or cloud platforms.
Showcase Your Projects: If you’ve got any projects that demonstrate your ability to take models from research to deployment, make sure to include them. We love seeing practical examples of your work, especially if they relate to MLOps or financial services.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Edison Smart
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially those relevant to financial services. Be ready to discuss your experience with ML libraries like TensorFlow and PyTorch, and how you've deployed models in production. This will show that you’re not just familiar with the theory but can also apply it practically.
✨Showcase Your Collaboration Skills
Since this role involves working closely with Data Scientists, Software Engineers, and other teams, be prepared to share examples of how you've successfully collaborated in the past. Highlight any projects where teamwork was key to deploying a model or improving an ML pipeline.
✨Demonstrate Your Engineering Mindset
The interviewers will want to see that you appreciate robust engineering practices. Talk about your experience with building end-to-end ML pipelines and how you ensure models are resilient and performant. Mention any best practices you follow for model governance and monitoring.
✨Get Familiar with MLOps
If you have experience with MLOps tools like MLflow or Kubeflow, make sure to mention it. Even if you haven't used them extensively, showing that you understand their importance in model deployment and monitoring can set you apart. Be ready to discuss how you would implement these practices in a regulated environment.
