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, flexible work environment, and opportunities for ownership.
- Other info: Join a small, dynamic team focused on innovation and growth.
- Why this job: Be part of a fast-paced team making real financial impacts with cutting-edge technology.
- Qualifications: 3+ years in FinTech, strong ML infrastructure experience, and a proactive attitude.
The predicted salary is between 60000 - 80000 £ per year.
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 in London 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 in London
✨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 involve pipelines or deployment. This will give potential employers a taste of what you can do and how you tackle real-world problems.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of compliance and reliability in FinTech. Be ready to discuss how you've handled these aspects in past roles — it’s crucial for this position!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talent like yours, and applying directly can sometimes give you an edge. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace FinTech MLOps Engineer in London
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 understand the impact of AI on credit scoring and how your skills can contribute to this transformation.
Highlight Relevant Experience:Make sure to showcase your experience in ML infrastructure and any projects where you've deployed models in production. We’re looking for someone who can hit the ground running, so don’t hold back on those details!
Demonstrate Problem-Solving Skills:In your application, share examples of how you've tackled challenges in previous roles. We value a proactive attitude, so show us how you identify problems and take action without waiting for permission.
Tailor Your Application:Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific requirements of the MLOps Engineer role. We appreciate when candidates take the time to connect their skills with what we’re looking for.
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 how you've handled deployment and monitoring.
✨Showcase Your Problem-Solving Skills
This company values attitude and ownership, so come ready with examples of how you've identified problems and taken initiative to solve them. Think of situations where you made a plan and acted 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 a regulated environment.
✨Demonstrate Technical Proficiency
Be prepared to dive deep into your technical skills. Know your way around Python, ML tools like MLflow or Kubeflow, and cloud platforms like AWS or GCP. You might even want to bring up specific projects where you used containerization and orchestration tools like Docker and Kubernetes.
