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 on innovative AI projects.
- Other info: Collaborative environment with opportunities to explore generative AI technologies.
- 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 employer: Investigo
Join a forward-thinking team in Nottingham as a Machine Learning Engineer, where innovation meets collaboration. Our company fosters a dynamic work culture that prioritises employee growth through hands-on experience with cutting-edge technologies and real-world datasets. Enjoy the flexibility of a hybrid work model while contributing to impactful AI solutions in a supportive environment that values your expertise and encourages continuous learning.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even local tech events. You never know who might have a lead on that perfect Machine Learning Engineer role.
✨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 gives potential employers a taste of what you can do and sets you apart from the crowd.
✨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 in detail. Confidence is key, so show them you know your stuff!
✨Apply Through Us
Don’t forget to check out our website for the latest job openings! Applying directly through us not only streamlines the process but also gives you access to exclusive opportunities in the field of Machine Learning Engineering.
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, C#, and any relevant ML frameworks. We want to see how your skills match up with 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. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects:If you've worked on any cool projects related to ML or AI, make sure to mention them! Whether it's a personal project or something from a previous job, we want to see your hands-on experience and creativity in action.
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 be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Investigo
✨Know Your Tech Stack
Make sure you’re well-versed in Python and C#/.NET, as these are crucial for the role. Brush up on your knowledge of ML frameworks like TensorFlow and PyTorch, and be ready to discuss how you've used them in past projects.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built and deployed production-grade ML systems. Highlight your experience with data pipelines, feature engineering, and any generative AI capabilities you've explored. Real-world examples will make you stand out!
✨Understand MLOps
Familiarise yourself with MLOps concepts such as model monitoring and CI/CD automation. Be ready to discuss how you’ve implemented these practices in your previous roles, as this shows you can support end-to-end processes effectively.
✨Collaborate and Communicate
Since this role involves working closely with cross-functional teams, practice articulating your thoughts clearly. Think of examples where you’ve successfully collaborated with engineers or domain specialists, as strong communication skills are key.
