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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 Woking employer: Edison Smart®
Contact Detail:
Edison Smart® Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Woking
✨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, and don’t forget to highlight your understanding of model governance and explainability.
✨Tip Number 4
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 Woking
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, 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 will show that you’re not just familiar with the theory but have practical experience too.
✨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
This position requires a strong appreciation for robust engineering and scalability. Talk about your experience with building end-to-end ML pipelines and how you ensure models meet performance and resilience requirements. Mention any best practices you follow for MLOps and model governance.
✨Be Ready for Technical Questions
Expect some technical questions during the interview. Brush up on data pipelines, versioning, and testing practices. You might also want to prepare for scenario-based questions where you’ll need to explain how you would handle model drift detection or ongoing optimisation in a production environment.
