At a Glance
- Tasks: Design and maintain ML pipelines, ensuring robust model monitoring and compliance.
- Company: Stealth UK FinTech transforming credit scoring with AI.
- Benefits: Competitive salary, dynamic team, and opportunity for ownership.
- Other info: Join a small team that values initiative and problem-solving.
- Why this job: Be part of a fast-paced team making real financial impacts.
- Qualifications: 3+ years in FinTech, strong ML infrastructure experience, and Python skills.
The predicted salary is between 60000 - 80000 £ per year.
Overview: This stealth UK FinTech is using AI to transform how credit scoring works. They’ve developed proprietary models that are already proving their value — now they need an MLOps engineer to build the infrastructure that takes them to scale. You’ll own the full ML lifecycle: pipelines, deployment, monitoring, and continuous improvement. This is FinTech, so reliability and compliance aren’t optional — the systems you build will power real financial decisions for real people. The team is small and moves fast. They care deeply about attitude: they want someone who sees problems, makes a plan, and acts. No waiting around for permission. If you thrive on ownership and want to be part of something from the ground up, this is it.
Responsibilities:
- Design and maintain ML pipelines for training, validation, and deployment
- Build robust model monitoring and observability — detecting drift, degradation, and anomalies before they become problems
- Implement feature stores and data pipelines that serve real‑time and batch inference
- Own the CI/CD for ML models — automated testing, canary deployments, and rollback strategies
- Ensure compliance and auditability of model decisions in a regulated environment
- Collaborate with data scientists to move models from notebooks to production
Qualifications:
- 3+ years experience in FinTech or financial services — you understand the domain
- Strong experience with ML infrastructure — you’ve deployed and operated models in production
- Comfortable with Python and the modern ML tooling ecosystem (MLflow, Kubeflow, Airflow, or similar)
- Deep understanding of containerization and orchestration (Docker, Kubernetes)
- Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure‑as‑code
- The right attitude — you see problems, you plan, you act. No hand‑holding required
FinTech MLOps Engineer employer: Major UK Grocery Retailer
This innovative UK FinTech offers a dynamic work environment where you can take ownership of your projects and make a real impact on the future of credit scoring. With a strong emphasis on collaboration and a culture that values proactive problem-solving, employees are encouraged to grow their skills in a fast-paced setting. Located in the heart of the UK, this company provides unique opportunities for professional development while being part of a mission-driven team that is transforming financial decisions for individuals.
StudySmarter Expert Advice🤫
We think this is how you could land FinTech MLOps Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the FinTech space, especially those who work with MLOps. Use LinkedIn to connect and engage with them — you never know who might have a lead on that perfect job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially any that relate to credit scoring or financial services. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you've tackled challenges in previous roles, particularly around building ML pipelines and ensuring compliance in regulated environments.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who are ready to take ownership and make an impact. Your next big opportunity could be just a click away!
We think you need these skills to ace FinTech MLOps Engineer
Some tips for your application 🫡
Show Your Passion for FinTech:When writing your application, let your enthusiasm for the FinTech industry shine through. We want to see that you’re not just looking for any job, but that you’re genuinely excited about transforming credit scoring with AI.
Highlight Relevant Experience:Make sure to showcase your experience in MLOps and financial services. We’re looking for someone who understands the domain and has hands-on experience with ML infrastructure, so don’t hold back on those details!
Demonstrate Problem-Solving Skills:We love candidates who take initiative! In your application, share examples of how you've tackled challenges in the past. Show us that you see problems, make a plan, and act without waiting for permission.
Apply Through Our Website:To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to see your application and get you into the process quickly!
How to prepare for a job interview at Major UK Grocery Retailer
✨Know Your ML Lifecycle
Make sure you can confidently discuss the full machine learning lifecycle. Be prepared to explain how you've designed and maintained ML pipelines in the past, and share specific examples of your experience with deployment and monitoring.
✨Showcase Your Problem-Solving Skills
This company values attitude and ownership, so come ready to demonstrate how you've tackled challenges in previous roles. Think of a few scenarios where you identified a problem, devised a plan, and took action without waiting for permission.
✨Familiarise Yourself with Compliance
Since this role is in FinTech, understanding compliance and auditability is crucial. Brush up on relevant regulations and be ready to discuss how you've ensured compliance in your previous projects, especially in relation to model decisions.
✨Get Comfortable with Tools and Tech
Be prepared to talk about your experience with Python and modern ML tools like MLflow, Kubeflow, or Airflow. Also, highlight your familiarity with containerisation and orchestration technologies like Docker and Kubernetes, as well as cloud platforms like AWS, GCP, or Azure.
