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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
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 Newcastle upon Tyne employer: Edison Smart®
Contact Detail:
Edison Smart® Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Newcastle upon Tyne
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even online forums. The more people you know, the better your chances of landing that Machine Learning Engineer gig.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those related to financial services. Use GitHub or a personal website to display your work. This gives potential employers a taste of what you can do!
✨Ace the Interview
Prepare for technical interviews by brushing up on your Python skills and ML libraries. Practice common interview questions and be ready to discuss your experience with deploying models in production. Confidence is key!
✨Apply Through Us!
Don’t forget to check out our website for the latest job openings. Applying through us not only gives you access to exclusive roles but also helps us support you throughout the process. Let’s get you that contract!
We think you need these skills to ace Machine Learning Engineer in Newcastle upon Tyne
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 production-grade systems.
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 shows you're not just familiar with the theory but can 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
Talk about your approach to building robust and scalable ML systems. Discuss your understanding of data pipelines, versioning, and testing. Employers want to see that you appreciate the engineering side of machine learning, so don’t shy away from technical details!
✨Be Ready for Real-World Scenarios
Prepare for questions that test your problem-solving skills in high-volume environments. Think of specific challenges you've faced in previous roles, especially around model governance and performance monitoring. Showing that you can handle real-world issues will set you apart from other candidates.
