At a Glance
- Tasks: Operationalise research models and build scalable systems for machine learning.
- Company: Dynamic fintech in Manchester with a focus on innovation and collaboration.
- Benefits: Hybrid remote work, competitive salary, and a supportive team culture.
- Other info: Inclusive culture that values diverse perspectives and offers excellent career growth.
- Why this job: Tackle complex problems and contribute to groundbreaking research in a collaborative environment.
- Qualifications: Experience in ML Ops, strong CI/CD skills, and proficiency in Python.
The predicted salary is between 63000 - 77000 £ per year.
Permanent Manchester, United Kingdom ML Ops / Model Deployment Engineer - Research Function Manchester (2 days a week in City Centre)We’re supporting the build of a new research function within a Manchester fintech that is developing a simulated digital twin allowing the team to experiment, optimise, and solve genuinely complex commercial problems in a controlled environment.
We recently placed the Lead Research Data Scientist, who as well as being a great guy, is a highly capable, exceptional and thoughtful leader.
We’re now helping to build the team around them.
ML Ops / Model Deployment (CI/CD, Scaling, Robustness)As the ML Ops Engineer, you’ll focus on operationalising research models—building the systems, pipelines and tooling that take experimental work and make it stable, scalable and production-ready.
Key Responsibilities CI/CD for machine learning models.
Automating testing, validation, packaging and deployment.
Building scalable, reliable pipelines for research prototypes.
Managing model registries, monitoring, versioning and observability.
Supporting integration of research models into wider engineering systems.
Ensuring reproducibility and robustness across the model lifecycle.
Ideal Profile Experience in ML Ops or production ML engineering.
Strong CI/CD tooling (Git Hub Actions, Git Lab CI, Azure Dev Ops).
Docker/Kubernetes and modern deployment frameworks (MLflow, Seldon, Bento ML etc).
Strong Python skills.
Cloud experience.
Bonus: experience with Bayesian / simulation-led modelling.
Why this stands out1??Research-first, not reporting. 2??Complex, multi-variable and commercially meaningful problems. 3??Warm, curious, collaborative and sharp culture. 4??Everyone we’ve placed here loves it — which says everything!Mirai believes in the power of diversity and the importance of an inclusive culture.
It welcomes applications from individuals of all backgrounds, understanding that a range of perspectives strengthens both its team and its partners’ teams.
This is just one of the ways they’re taking positive action to shape a collaborative and diverse future in the workplace.
Machine Learning Engineer - Hybrid Remote in Manchester employer: Mirai Tech Recruitment
Join a mission-driven organisation in London or Manchester as the Head of Data Science, where your work will directly impact the lives of the people you serve. With a strong focus on employee growth and a collaborative culture, you'll lead a multidisciplinary team to harness data for meaningful advocacy, all while enjoying a competitive salary, generous leave, and hybrid working options. This is an exceptional opportunity to contribute to a public sector initiative that values diversity and inclusivity, making it a truly rewarding place to work.
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We think this is how you could land Machine Learning Engineer - Hybrid Remote in Manchester
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We think you need these skills to ace Machine Learning Engineer - Hybrid Remote in Manchester
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
