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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 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
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 Hull employer: Edison Smart
Contact Detail:
Edison Smart Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Hull
✨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 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 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 and cloud platforms, as well as how you've tackled challenges in previous roles. 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 Hull
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 and tools. Be ready to discuss your experience with Python, TensorFlow, and PyTorch, as well as any specific projects you've worked on in financial services. This will show that you’re not just familiar with the tech but have practical experience applying it.
✨Showcase Your End-to-End Process
Prepare to talk about how you've taken models from research to deployment. Highlight your experience with building and maintaining ML pipelines, and be ready to discuss how you ensure performance, resilience, and explainability in your models. This is crucial for a role in a regulated environment.
✨Collaboration is Key
Since this role involves working closely with Data Scientists, Software Engineers, and other teams, think of examples where you’ve successfully collaborated on projects. Emphasise your communication skills and how you’ve contributed to team success in past roles.
✨MLOps and Governance Knowledge
Familiarise yourself with MLOps best practices and model governance. Be prepared to discuss any tools you've used like MLflow or Kubeflow, and how you approach model monitoring and optimisation. This will demonstrate your understanding of the importance of these practices in a financial services context.
