Machine Learning Engineer - Python & C# - Nottingham/ Hybrid
3 month contract initially. - Outside IR35
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.