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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 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 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 Kingston upon 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 Kingston upon 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 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 hiring managers.
We think you need these skills to ace Machine Learning Engineer in Kingston upon 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 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 or experiences 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. Mention 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 applications and ensures you don’t miss out on any important 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 any other libraries you've used. They’ll likely ask about specific projects, so have a couple of examples ready that showcase your skills in deploying models in production.
✨Understand the Financial Services Landscape
Since this role is within financial services, it’s crucial to understand the regulatory environment. Familiarise yourself with common challenges in this sector, like model governance and explainability. Showing that you grasp these nuances will set you apart from other candidates.
✨Collaboration is Key
This position involves working closely with various teams, so be prepared to talk about your collaborative experiences. Think of examples where you’ve worked with data scientists or software engineers to build end-to-end ML pipelines. Highlighting your teamwork skills can really make a difference.
✨Showcase Your MLOps Knowledge
If you have experience with MLOps tools like MLflow or Kubeflow, make sure to mention it! Discuss how you’ve implemented best practices for model monitoring and drift detection. This shows you’re not just about building models but also about maintaining them effectively in a production environment.