Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- 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 an impact by working on high-volume ML systems that drive real-world results.
- 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
Required Experience
- 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)
Nice to Have
- 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
Contract Details
- £650–£750 per day (Outside IR35)
- Initial 6-month contract, with strong extension potential
- Immediate or short-notice start preferred
Machine Learning Engineer in Plymouth employer: Edison Smart®
Contact Detail:
Edison Smart® Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Plymouth
✨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 opportunities that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best machine learning projects, especially those relevant to financial services. This will give potential employers a taste of what you can bring to the table and set you apart from the competition.
✨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 like TensorFlow and PyTorch, as well as your understanding of MLOps practices. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications this way.
We think you need these skills to ace Machine Learning Engineer in Plymouth
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 your experience with deploying ML models and your understanding of MLOps best practices. Keep it concise but impactful!
Showcase Relevant Projects: If you’ve worked on any projects that align with the responsibilities listed in the job description, make sure to include them. Whether it's building end-to-end ML pipelines or working with cloud platforms, we want to see what you've done!
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 updates from us!
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 have examples of models you've deployed in production.
✨Showcase Your Collaboration Skills
This role involves working closely with various teams, so be prepared to talk about how you've collaborated with Data Scientists, Software Engineers, and other stakeholders in the past. Highlight any successful projects where teamwork was key to your success.
✨Demonstrate Your Engineering Mindset
Since robust engineering and scalability are crucial, come equipped with examples of how you've built and maintained end-to-end ML pipelines. Discuss your approach to model governance and how you've ensured performance and resilience in your previous work.
✨Familiarity with MLOps is a Plus
If you've worked with MLOps tools like MLflow or Kubeflow, make sure to mention it! Even if you haven't, showing an understanding of model monitoring and drift detection will impress the interviewers and demonstrate your commitment to best practices.