At a Glance
- Tasks: Design and deploy machine learning models, develop scalable ML pipelines, and collaborate with cross-functional teams.
- Company: Leading global professional services organisation focused on advanced analytics and AI solutions.
- Benefits: Competitive salary, flexible working arrangements, and strong career development opportunities.
- Other info: Dynamic environment with significant investment in digital and AI transformation initiatives.
- Why this job: Join a collaborative team and tackle complex data challenges across various industries.
- Qualifications: Strong Python skills and experience with machine learning frameworks and production systems.
The predicted salary is between 90000 - 100000 £ per year.
Location: London (Hybrid/Other locations available)
Salary: Up to £100k + Benefits
The Opportunity
A leading global professional services organisation is expanding its advanced analytics and AI capabilities and is seeking a Machine Learning Engineer with strong production and MLOps experience to join its growing technology and data team. Operating across multiple sectors including financial services, public sector, healthcare, and technology, the organisation helps clients solve complex business challenges through data-driven insights, automation, and advanced AI solutions. With significant investment being made in its digital and AI transformation initiatives, the firm is building a team of engineers and data specialists responsible for delivering scalable machine learning systems that support real-world client applications. This role will sit within a collaborative team of data scientists, engineers, and technology consultants, working on the design and deployment of machine learning models that move beyond experimentation and into production environments.
Key Responsibilities
- Design, build, and deploy machine learning models into production environments
- Develop scalable ML pipelines and automated training workflows
- Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders
- Implement CI/CD processes for machine learning systems
- Monitor model performance, manage model drift, and optimise inference pipelines
- Build APIs and services to integrate machine learning capabilities into enterprise applications
- Contribute to the development and evolution of the organisation’s ML platform and infrastructure
Required Skills & Experience
- Strong programming skills in Python
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn
- Demonstrable experience deploying machine learning models into production systems
- Experience with Docker and Kubernetes for containerisation and orchestration
- Familiarity with MLOps tools such as MLflow, Kubeflow, Airflow, or similar
- Experience working with cloud platforms such as AWS, GCP, or Azure
- Strong understanding of data pipelines and large-scale data processing
- Experience with distributed data processing (e.g. Spark or Databricks)
- Knowledge of model monitoring and observability tools
- Experience building real-time inference systems or APIs
- Exposure to modern Generative AI or LLM frameworks
What’s on Offer
- Opportunity to work with a global professional services organisation delivering AI solutions to major clients
- Exposure to a wide range of industries and complex data challenges
- A collaborative, innovation-focused engineering environment
- Competitive salary and benefits package
- Flexible working arrangements (remote/hybrid)
- Strong opportunities for career development within a growing AI practice
Machine Learning Engineer employer: Nixor
As a leading global professional services organisation, we offer an exceptional work environment for Machine Learning Engineers, characterised by a collaborative and innovation-focused culture. Our commitment to employee growth is evident through extensive career development opportunities and exposure to diverse industries, allowing you to tackle complex data challenges while enjoying flexible working arrangements. Join us in shaping the future of AI solutions and be part of a team that values your contributions and fosters your professional journey.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how important it is to make connections; you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend including links to GitHub repos or any live demos. This gives potential employers a taste of what you can do beyond just a CV.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your past projects in detail. We suggest practicing common ML interview questions and even doing mock interviews with friends.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for talented individuals like you to join our team and help us tackle exciting challenges in AI.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, MLOps, and any relevant frameworks like PyTorch or TensorFlow. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background makes you a great fit for our team. Let us know what excites you about working in a collaborative environment.
Showcase Your Projects:If you've worked on any machine learning projects, be sure to include them! Whether it's a personal project or something from a previous job, we love seeing real-world applications of your skills. Share links or descriptions that demonstrate your expertise.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll find all the details you need there. Plus, it helps us keep track of your application better!
How to prepare for a job interview at Nixor
✨Know Your Tech Inside Out
Make sure you’re well-versed in the programming languages and frameworks mentioned in the job description, especially Python, PyTorch, and TensorFlow. Brush up on your MLOps knowledge too, as they’ll likely ask about your experience with tools like Docker and Kubernetes.
✨Showcase Real-World Applications
Prepare to discuss specific projects where you've deployed machine learning models into production. Highlight any challenges you faced and how you overcame them, especially in terms of model monitoring and optimisation.
✨Collaborate Like a Pro
Since this role involves working with cross-functional teams, be ready to talk about your collaboration experiences. Share examples of how you’ve worked with data scientists and software engineers to deliver successful projects.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in their AI initiatives. Inquire about their current projects or how they envision the evolution of their ML platform. This shows you’re not just interested in the role, but also in the company’s future.
