At a Glance
- Tasks: Design and build production-ready ML models on Google Cloud Platform.
- Company: Join a forward-thinking tech company focused on real-world solutions.
- Benefits: Earn £600 per day, outside IR35, with flexible contract terms.
- Other info: 6-month contract with opportunities for growth in a dynamic environment.
- Why this job: Make a tangible impact by turning concepts into scalable ML systems.
- Qualifications: Hands-on experience with GCP and strong Python skills required.
The predicted salary is between 48000 - 72000 € per year.
6-Month Contract | Outside IR35 | £600 per day
We are seeking an experienced Machine Learning Engineer to support the design and build, production ready ML models on Google Cloud Platform (GCP). This is a hands‑on delivery role, focused on turning models into scalable, reliable, production systems that solve real business problems. The contract will run for at least 6 months, will be Outside IR35 at £600 per day, and we are looking to start the project at the beginning of March. This role suits a delivery‑focused ML Engineer who enjoys taking models from concept through to production, rather than staying purely in research or experimentation.
Key Responsibilities- Design, build, and productionise machine learning models using GCP-native services
- Translate business problems into deployable ML solutions
- Develop and maintain end‑to‑end ML pipelines (training, testing, deployment, monitoring)
- Work with data scientists and engineers to operationalise models at scale
- Implement best practices for model performance, versioning, and lifecycle management
- Ensure solutions are secure, scalable, and cost‑efficient within GCP
- Strong hands‑on experience building and deploying ML models on Google Cloud Platform
- Experience with services such as Vertex AI, BigQuery, Cloud Storage, and Cloud Functions / Cloud Run
- Solid Python experience for ML and data engineering workloads
- Experience productionising models (not just experimentation or notebooks)
- Understanding of MLOps concepts: CI/CD, monitoring, retraining, and model governance
- Ability to work independently in a contract environment and deliver at pace
- Experience with real‑time or near‑real‑time ML use cases
- Exposure to data pipelines and orchestration tools
- Prior work in regulated or large‑scale enterprise environments
- Duration: 6 months
- Rate: £500 per day
- IR35: Outside IR35
- Start: March 2026
To learn more about this opportunity, please send your CV to Method Resourcing for consideration.
Contract Machine Learning Engineer (GCP) 6-Months £600 employer: Armymkt
Join a forward-thinking company that values innovation and hands-on delivery in the field of machine learning. With a strong focus on employee growth, we offer a collaborative work culture where your contributions directly impact real business solutions. Located in a vibrant tech hub, this role provides unique opportunities to work with cutting-edge technologies on Google Cloud Platform, ensuring you stay at the forefront of the industry while enjoying competitive compensation and flexible contract terms.
StudySmarter Expert Advice🤫
We think this is how you could land Contract Machine Learning Engineer (GCP) 6-Months £600
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. You never know who might have the inside scoop on a job opportunity that’s perfect for you.
✨Tip Number 2
Showcase your skills! Create a portfolio or GitHub repository with your ML projects, especially those using GCP. This gives potential employers a tangible look at what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding of MLOps concepts. Be ready to discuss how you've turned models into production systems and how you tackle real business problems.
✨Tip Number 4
Apply through our website! We make it easy for you to find roles that match your skills. Plus, it shows you're serious about joining our team and helps us get to know you better right from the start.
We think you need these skills to ace Contract Machine Learning Engineer (GCP) 6-Months £600
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your hands-on experience with GCP and ML models. We want to see how you've turned concepts into production-ready systems, so don’t hold back on those details!
Showcase Relevant Projects:Include specific projects where you've built and deployed ML models using GCP-native services. We love seeing real examples of your work, especially if they relate to the key responsibilities listed in the job description.
Highlight Your Skills:Don’t forget to mention your Python expertise and any experience with MLOps concepts. We’re looking for someone who can hit the ground running, so make sure we know you’ve got the skills to back it up!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Armymkt
✨Know Your GCP Inside Out
Make sure you brush up on your Google Cloud Platform knowledge, especially services like Vertex AI and BigQuery. Be ready to discuss how you've used these tools in past projects, as this will show your hands-on experience and understanding of the platform.
✨Showcase Your ML Pipeline Skills
Prepare to talk about your experience with end-to-end ML pipelines. Think of specific examples where you've designed, built, and deployed models, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Your MLOps Knowledge
Familiarise yourself with MLOps concepts like CI/CD and model governance. Be prepared to discuss how you've implemented best practices for model performance and lifecycle management in your previous roles.
✨Be Ready for Real-World Scenarios
Expect questions that focus on translating business problems into deployable ML solutions. Think of real-world examples where your work made a tangible impact, and be ready to explain your thought process and decision-making.
