At a Glance
- Tasks: Architect and develop cutting-edge AI/ML systems and solutions.
- Company: Join a leading global financial services organisation investing heavily in AI.
- Benefits: Competitive salary, influence technical direction, and work on innovative projects.
- Other info: Opportunity for mentorship and significant career growth in a dynamic environment.
- Why this job: Make a real impact by building next-gen AI capabilities from the ground up.
- Qualifications: 12+ years in production AI/ML systems with strong Python and ML expertise.
The predicted salary is between 63000 - 77000 £ per year.
We're working with a leading global financial services organisation that is making a significant investment in AI and Machine Learning, building new AI Innovation teams to develop and deploy next-generation AI solutions.
This is an opportunity for an experienced
Machine Learning Engineer to play a key technical role in designing and building greenfield AI/ML platforms, including sophisticated agentic AI systems, LLM-powered applications and intelligent automation.
We're looking for someone with a strong background in production ML engineering , rather than a predominantly research or Data Science background.
You'll need to be comfortable taking complex AI solutions from initial concept and experimentation through to scalable, enterprise-grade production systems.
This is a senior, hands-on individual contributor position, with responsibility for technical architecture, engineering standards and mentoring other engineers, but no direct line management.
The Role
- Architect and develop production-grade ML/AI systems, including Gen AI platforms, LLM applications and agentic workflows.
- Design and implement RAG architectures, multi-agent systems and intelligent automation solutions.
- Lead technical decisions around ML architecture, frameworks, model deployment and infrastructure.
- Develop and deploy ML models using Python, Py Torch and modern ML frameworks.
- Build scalable APIs, MLOps pipelines and cloud-native infrastructure to support production AI systems.
- Take AI concepts and proofs of concept through to reliable, scalable production solutions.
- Establish engineering best practices around testing, CI/CD, model monitoring, performance and AI governance.
- Provide technical guidance and mentorship while remaining actively involved in hands-on engineering.
What We're Looking For
12+ years' professional experience building production AI/ML systems , with a strong software engineering foundation.
- Extensive hands-on Python experience and strong knowledge of ML algorithms, deep learning and frameworks such as Py Torch.
- Proven experience architecting and delivering scalable ML platforms and solutions from the ground up.
- Advanced practical experience with
Generative AI, LLMs, RAG, agentic AI and multi-agent systems .
- Experience with model training, fine-tuning, evaluation and production deployment.
- Strong MLOps knowledge, including Docker, Kubernetes, CI/CD, model monitoring and workflow orchestration.
- Experience with AWS or Azure, along with API development using frameworks such as Fast API.
- Ability to make and justify complex architectural decisions, including technology selection and engineering trade-offs.
- Experience mentoring engineers and providing technical leadership without necessarily managing a team.
- A degree in Computer Science, Machine Learning, Data Science, Applied Mathematics or a related discipline.
Why Consider It?
This is an opportunity to join a well-funded AI transformation programme with significant executive backing, where you'll have genuine influence over technical direction and the opportunity to build new ML capabilities rather than simply maintain existing systems.
You’ll be working on challenging problems involving NLP, document intelligence, reasoning systems and intelligent automation, with access to modern AI infrastructure and the opportunity to influence how AI is adopted across a global organisation.
If you're an experienced, hands-on ML Engineer who enjoys designing complex systems, solving difficult engineering problems and turning emerging AI technologies into real production solutions, I'd be interested in speaking with you.
Senior Machine Learning Engineer employer: James Chase
As a Data Excel Specialist in Crawley, you will join a dynamic team that values innovation and collaboration, offering a hybrid work model that promotes work-life balance. The company is committed to employee growth, providing opportunities to enhance your skills in HRIS and data management while playing a pivotal role in a large-scale transformation programme. With a focus on continuous improvement and a supportive culture, this is an excellent employer for those seeking meaningful and rewarding employment.
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We think this is how you could land Senior Machine Learning Engineer
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We think you need these skills to ace Senior Machine Learning Engineer
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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Craft a Tailored Cover Letter:For a full-time role at James Chase, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at James Chase. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at James Chase
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
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.
