We’re launching an exciting new engagement to develop an AI-powered robotic welding solution, with work beginning in early May. This 4–6 week initial engagement will focus on aligning with the client, refining the delivery roadmap, and beginning hands-on development of key machine learning capabilities. We’re looking for a Senior ML Engineer with deep expertise in computer vision, workflow automation, and robotic systems. You’ll help design, develop, and deploy intelligent ML solutions that power real-world robotics in a high-impact, greenfield setting. The project will run in parallel streams across development, infrastructure, and platform — ideal for a collaborative engineer who thrives in fast-paced environments. Preference will be given to candidates who currently hold, or are eligible to obtain, MOD security clearance in the UK. Key Responsibilities • Design, develop, and deploy ML models for image classification and robotics (e.g., YOLO, ResNet) • Collaborate closely with engineering and infrastructure teams to align delivery with client needs • Build, train, and validate ML models using libraries such as Pandas, NumPy, TensorFlow, and Scikit-learn • Implement and optimize automation workflows using Node-RED or ServiceNow Flow Designer • Create unit tests and CI/CD pipelines to support robust, production-grade model deployment • Participate in agile sprints with infrastructure and platform engineers • Tune models for performance and reliability in real-time environments Ideal Experience • 5+ years of experience in ML, data science, or applied AI roles • Strong background in computer vision, including model development and tuning • Expert-level Python skills, especially with libraries like Pandas, NumPy, TensorFlow, and Scikit-learn • Experience implementing image classification models (e.g., YOLO, ResNet) • Familiarity with automation tools and data flow systems like Node-RED and ServiceNow Flow Designer • Strong understanding of JSON and JavaScript for flow development • Previous exposure to robotics, autonomous systems, or real-world ML deployments • Comfortable working in fast-paced, client-facing projects • Experience testing and validating ML models for performance, robustness, and edge cases • Bonus: Exposure to ML Ops, containerization (Docker/Kubernetes), or edge computing environments [PUBLISHED]