At a Glance
- Tasks: Design and optimise machine learning models while collaborating with clients on innovative projects.
- Company: Join a leading Data & Analytics Consulting team in London.
- Benefits: Enjoy competitive salary, 25 days leave, private medical insurance, and training opportunities.
- Other info: Hybrid role with excellent career growth and mentoring opportunities.
- Why this job: Make an impact with cutting-edge AI technology and develop your skills in a dynamic environment.
- Qualifications: 3-5+ years in machine learning engineering with strong Python and SQL skills.
The predicted salary is between 63000 - 77000 £ per year.
Snr Machine Learning Engineer - Client facing - 12 months FTC
Department
Project Delivery
Employment Type
Permanent - Full Time
Location
- London
- Description
The Machine Learning Engineer role sits within Client Delivery, embedded in the Data & Analytics Consulting (DACs) team.
A technical, client‑facing group of Data Scientists and ML Engineers responsible for building and operationalising advanced machine learning and AI
This is a 12 months contract with potential to become a perm role afterwards.
Key Responsibilities
- Machine learning and AI engineering
- Design, train and optimise predictive models (gradient boosted trees, temporal and embedding-based models), with reproducible training, evaluation and monitoring pipelines.
- Productionise models to run reliably at scale in client environments.
- Build and integrate LLM components (embedding pipelines, RAG, agentic systems) and NLP models for extraction, classification and risk prediction.
- Data engineering
- Own versioned, validated data flows for modelling and inference, including schema design and SQL optimisation for large training and scoring workloads.
- Extract data from client source systems and build cohort logic that is accurate and interpretable.
- Technical delivery
- Lead the modelling and data engineering components of client projects alongside DACs and Business Consultants, resolving complex modelling and pipeline issues.
- Configure One View components linked to modelling outputs (risk logic, summaries, scoring pathways) and feed engineering improvements back into the platform.
- Knowledge sharing and technical leadership
- Act as SME for machine learning and AI within DACs.
- Mentor DACs on Python, modelling practice and debugging.
- Produce documentation, templates and reusable components that raise delivery standards.
What are we looking for?
- 3–5+ years’ experience in machine learning engineering, taking models from development into production.
- Strong Python skills and experience with modern ML frameworks, including training and evaluating tree-based, temporal, NLP or LLM-based models.
- Experience building reproducible training and evaluation pipelines, and containerising and deploying models (for example, Docker, Fast API).
- Strong SQL, a solid grounding in relational databases, schemas and data transformations (ideally DBT), and experience preparing data for model training and scoring.
- Hands-on AI experience: embeddings, vector databases or RAG workflows, and NLP or sequence models applied to real-world datasets.
- Experience delivering technical work to clients or stakeholders, including defining data requirements and troubleshooting in real time.
- Clear communication: you explain technical concepts simply and work closely with data scientists, engineers and consultants.
Bonus points if you have
- Experience with Azure ML, AKS or similar cloud environments.
- Experience with public sector datasets or analytical workflows.
- Location –
This is a hybrid role based in our office in London (Borough).
You would be expected to be able to work from the office 1-2 days per week.
Some travel may be required for on-site client engagements.
What can we offer you?
- Competitive salary reviewed annually
- Training and development opportunities
- 25 days annual leave (plus bank holidays)
- Company pension
- Private medical insurance
- Generous enhanced parental leave policies
- Cycle to work scheme
- Flu Vaccinations,
- Eye Test and contribution towards Glasses for VDU use
- Employee Assistance Programme
- Mental health and wellbeing support
- Remote GP access
- Counselling/therapy
- Physiotherapy
- Medical second opinions
- #J-18808-Ljbffr
Snr Machine Learning Engineer - Client facing - 12 months FTC employer: Xantura
Xantura is an exceptional employer that champions innovation and ethical practices in data analytics, making a significant impact in the public sector. With a strong focus on employee growth, we offer a collaborative work culture that encourages creativity and professional development, ensuring our team members thrive in their roles. Located in a vibrant area, our workplace provides unique advantages such as access to cutting-edge technology and a supportive community dedicated to meaningful outcomes.
StudySmarter Expert Advice🤫
We think this is how you could land Snr Machine Learning Engineer - Client facing - 12 months FTC
✨Tap into Online Data Science Communities
Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like Xantura before they're even advertised!
✨Show Off Your Skills With Projects
Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
✨Check Out Specialist Job Boards
For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like Xantura.
✨Leverage University Resources
If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like Xantura.
We think you need these skills to ace Snr Machine Learning Engineer - Client facing - 12 months FTC
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Xantura, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Xantura, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Xantura’s attention and show the tangible impact of your work.
How to prepare for a job interview at Xantura
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Xantura.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Xantura.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Xantura.
