At a Glance
- Tasks: Design and deploy cutting-edge machine learning solutions for real-world applications.
- Company: Join a forward-thinking analytics team in Nottingham with a hybrid work model.
- Benefits: Competitive pay, flexible working, and the chance to work with advanced AI technologies.
- Other info: Collaborative environment with opportunities to explore generative AI and MLOps.
- Why this job: Make an impact by developing AI solutions that drive operational insights.
- Qualifications: Experience in ML engineering, strong Python and C# skills required.
The predicted salary is between 50000 - 60000 € per year.
We're looking for an experienced ML Engineer to join a cutting-edge advanced analytics team developing AI-powered solutions. This role offers the opportunity to work with large-scale real-world datasets including high-frequency sensor and SCADA data, building production-grade machine learning systems that deliver actionable operational insights. You’ll collaborate closely with data scientists, software engineers, and domain specialists to develop intelligent analytics platforms leveraging modern ML, MLOps, and generative AI technologies within a highly collaborative engineering environment.
Key Responsibilities:
- Design, develop, and deploy production-grade machine learning solutions for predictive analytics and fault detection.
- Build scalable ML inference services and APIs using Python and C#/.NET.
- Develop robust data pipelines and feature-engineering workflows across large industrial datasets.
- Apply signal processing and machine learning techniques to operational data.
- Implement and optimise model inference pipelines.
- Develop and maintain containerised ML workloads using Docker and cloud-native tooling.
- Collaborate cross-functionally with engineering, analytics, and domain experts.
- Contribute to CI/CD automation, testing frameworks, code reviews, and software engineering best practices.
- Support end-to-end MLOps processes including deployment, monitoring and model validation.
- Explore and implement generative AI capabilities including LLMs, RAG pipelines, and intelligent workflow automation.
Key Skills:
- Commercial experience in Machine Learning Engineering, Applied AI, or related software engineering roles.
- Strong programming skills in Python and C#/.NET.
- Experience building and deploying production ML systems and APIs.
- Hands-on knowledge of ML frameworks such as TensorFlow, PyTorch, scikit-learn, or similar.
- Experience with cloud platforms and modern data infrastructure (AWS preferred).
- Familiarity with Docker, CI/CD pipelines, and scalable deployment practices.
- Understanding of MLOps concepts including experiment tracking, model monitoring, and reproducibility.
- Exposure to Generative AI technologies including LLMs, RAG, or prompt engineering is a plus.
- Strong communication skills and ability to work effectively within cross-functional agile teams.
Machine Learning Engineer in Nottingham employer: Investigo
Join a forward-thinking team in Nottingham as a Machine Learning Engineer, where you'll be at the forefront of developing AI-powered solutions that drive real-world impact. Our collaborative work culture fosters innovation and growth, providing you with opportunities to enhance your skills in cutting-edge technologies like generative AI and MLOps. With a focus on employee development and a supportive environment, we ensure that your contributions are valued and recognised, making this an excellent place for meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in Nottingham
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even local tech events. The more people you know, the better your chances of landing that Machine Learning Engineer gig.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving Python and C#. Share your work on GitHub or personal websites. This is your chance to demonstrate your expertise in building production-grade ML systems!
✨Ace the Interview
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common interview questions and be ready to discuss your past projects. Remember, confidence is key!
✨Apply Through Us
Don’t forget to check out our website for the latest job openings! Applying directly through us can give you an edge, as we’re always on the lookout for talented individuals like you to join our advanced analytics team.
We think you need these skills to ace Machine Learning Engineer in Nottingham
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Python and C#. We want to see how you've tackled machine learning projects, especially those involving large datasets or production systems.
Showcase Your Projects:Include specific examples of your work with ML frameworks like TensorFlow or PyTorch. If you've built APIs or deployed models, let us know! Real-world applications speak volumes.
Be Clear and Concise:When writing your cover letter, keep it straightforward. Explain why you're a great fit for the role and how your skills align with our needs. We appreciate clarity!
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 don’t miss any important updates from our team.
How to prepare for a job interview at Investigo
✨Know Your Tech Stack
Make sure you’re well-versed in Python and C#. Brush up on your knowledge of ML frameworks like TensorFlow and PyTorch. Be ready to discuss how you've used these technologies in past projects, especially in building production-grade ML systems.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in machine learning projects. Think about how you designed and deployed solutions for predictive analytics or fault detection. Use real examples to illustrate your thought process and the impact of your work.
✨Understand MLOps Inside Out
Familiarise yourself with MLOps concepts such as model monitoring and reproducibility. Be prepared to discuss your experience with CI/CD pipelines and how you’ve contributed to automation in your previous roles. This will show that you can support end-to-end processes effectively.
✨Collaborate and Communicate
Since this role involves working closely with cross-functional teams, practice articulating your ideas clearly. Think of examples where you collaborated with data scientists or software engineers. Highlight your communication skills and how they helped achieve project goals.
