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For Companies At a Glance
- Tasks: Design and deploy machine learning models to tackle complex business challenges.
- Company: Join a leading telecoms company revolutionising customer service with innovative tech.
- Benefits: Enjoy remote work, a competitive salary, bonus, and a strong pension plan.
- Why this job: Be part of a fast-paced environment that values tech passion and collaboration.
- Qualifications: 4+ years in machine learning engineering with expertise in Python and cloud platforms.
- Other info: Monthly travel to London required; perfect for tech enthusiasts eager to make an impact.
The predicted salary is between 45000 - 105000 £ per year.
Job Description
Machine Learning Engineer
London – (3 days a week in office)
Up to £90,000
About the Role
Our client is a data-driven organisation focused on delivering measurable operational and financial improvements across a range of industries. They combine deep technical expertise with real-world delivery to design, build and deploy machine learning solutions that create tangible business impact.
As a Machine Learning Engineer, you’ll work closely with Data Scientists, Data Engineers and delivery teams to productionise models and build robust, scalable ML systems within client environments. This role is highly hands-on, with a strong focus on deployment, performance and reliability.
Key Responsibilities
- Translating data science models into scalable, production-ready machine learning solutions.
- Designing and building end-to-end ML pipelines, from data ingestion to deployment and monitoring.
- Collaborating with data engineers on data architecture, pipelines and feature stores.
- Working closely with data scientists to productionise models and improve performance.
- Deploying, monitoring and maintaining machine learning models in live environments.
- Implementing testing, validation and monitoring frameworks to ensure model reliability and impact.
Your work will focus on delivering high-value ML solutions, including:
- Productionising optimisation and predictive models at scale.
- Building systems to anticipate and prevent operational downtime.
- Deploying churn and customer behaviour models into live decision-making systems.
- Enabling next-best-action and recommendation engines for commercial teams.
- Supporting geospatial and advanced analytical models with robust ML infrastructure.
What We’re Looking For
- 2+ years’ experience building and deploying production machine learning systems.
- A strong academic background (Bachelor’s degree 2:1 or above in a quantitative subject).
- Strong Python skills and experience with ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch).
- Experience with model deployment, monitoring and ML pipelines (e.g. CI/CD, MLOps concepts).
- A collaborative mindset with strong communication skills and experience working in cross-functional teams.
If this role looks of interest, apply below.
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in MLOps and machine learning engineering. Being able to discuss recent advancements or tools during your conversations can demonstrate your passion and commitment to the field.
✨Tip Number 2
Network with professionals in the telecoms and machine learning sectors. Attend relevant meetups or webinars, and connect with people on LinkedIn. This can help you gain insights into the company culture and potentially get a referral.
✨Tip Number 3
Prepare to discuss specific projects where you've successfully implemented MLOps practices. Be ready to explain the challenges you faced and how you overcame them, as this will showcase your problem-solving skills and experience.
✨Tip Number 4
Research the company’s current products and services, especially those related to machine learning. Understanding their business model and how they leverage technology can help you tailor your discussions and show your genuine interest in their work.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning engineering, especially focusing on productionising ML models and MLOps. Include specific projects where you've designed, built, or deployed scalable machine learning models.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for technology and your ability to work in fast-paced environments. Mention your proficiency in Python and relevant ML libraries, as well as your experience with cloud platforms and CI/CD pipelines.
Showcase Relevant Projects: In your application, include examples of past projects that demonstrate your hands-on experience with MLOps, model versioning, and troubleshooting in production environments. This will help illustrate your capability to solve real-world business challenges.
Highlight Communication Skills: Since the role requires collaboration with data scientists and cross-functional teams, emphasise your excellent communication skills in your application. Provide examples of how you've effectively worked within teams to achieve project goals.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and ML libraries like TensorFlow and PyTorch. Bring examples of projects where you've successfully implemented machine learning models, focusing on the challenges you faced and how you overcame them.
✨Demonstrate MLOps Knowledge
Since the role heavily involves MLOps, make sure to highlight your experience with CI/CD pipelines and cloud platforms. Discuss specific tools you've used, such as Docker or Kubernetes, and how they contributed to the success of your projects.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would handle model deployment issues or performance monitoring, and be ready to explain your thought process clearly.
✨Emphasise Collaboration Skills
This role requires working closely with data scientists and cross-functional teams. Share examples of how you've effectively communicated and collaborated in past roles, highlighting any successful projects that resulted from teamwork.
