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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 a real impact by working on high-volume ML systems that drive financial solutions.
- Qualifications: Proven experience in machine learning, strong Python skills, and familiarity with cloud platforms.
- Other info: Collaborative team environment with opportunities for professional growth and development.
We’re 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 Shrewsbury employer: Edison Smart®
Contact Detail:
Edison Smart® Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Shrewsbury
✨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 best practices. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find and apply for roles that match your skills. Plus, we’re always on the lookout for talented individuals like you to join our community.
We think you need these skills to ace Machine Learning Engineer in Shrewsbury
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 showcase your Python skills and familiarity with relevant libraries.
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 projects you've worked on that align with the responsibilities listed in the job description.
Showcase Your Technical Skills: Don’t forget to emphasise your technical skills in your application. Detail your experience with cloud platforms and MLOps tools, as well as any hands-on work with Docker or Kubernetes. We love seeing practical examples!
Apply Through Our Website: We encourage you to apply through our website for a smoother 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 and tools. Be ready to discuss your experience with Python, TensorFlow, and other libraries mentioned in the job description. Prepare to explain how you've taken models from research to deployment, especially in a regulated environment.
✨Showcase Your Collaboration Skills
This role involves working closely with various teams, so be prepared to talk about your experience collaborating with Data Scientists, Software Engineers, and Product teams. Share specific examples of how you’ve contributed to team projects and how you handle differing opinions.
✨Demonstrate Your MLOps Knowledge
Familiarise yourself with MLOps best practices and tools like MLflow or Kubeflow. Be ready to discuss how you ensure model governance, performance, and resilience in production. Highlight any experience you have with monitoring and optimising models post-deployment.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to machine learning and their expectations for the role. This shows your genuine interest and helps you gauge if the company is the right fit for you. Consider asking about their current ML projects or challenges they face in the financial services sector.
