Physical AI Field Delivery Engineer (Computer Vision/Edge AI/ROS2) – Remote EEA
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Job description
Physical AI Field Delivery Engineer (Computer Vision/Edge AI/ROS2) – Remote EEA (50% Travel)
ABOUT THE OPPORTUNITY
This is a rare engineering role that sits at the intersection of AI, robotics, and industrial operations — and actually ships into the real world. You’ll be designing and deploying Physical AI solutions in environments like manufacturing plants, energy infrastructure, logistics hubs, and smart facilities, working directly with customers from discovery through to production. If you’ve built computer vision or edge AI systems that run on physical hardware in industrial settings — not just in notebooks — and you’re comfortable leading technical workshops as much as writing optimised inference code, this is the scope you’ve been looking for.
The role is remote-first within the EEA, with approximately 50% travel to client sites across Europe.
PROJECT & CONTEXT
You’ll work as a Field Delivery Engineer on Physical AI initiatives — spanning computer vision, robotics, edge computing, and industrial IoT — for customers in sectors such as manufacturing, energy, transportation, logistics, and smart infrastructure. Engagements run from requirements gathering and PoC through to full production deployment and ongoing optimisation. You’ll collaborate with solution architects and engineering teams, lead customer-facing workshops, and own the technical delivery of production-grade AI applications leveraging cameras, LiDAR, drones, robotics platforms, and edge computing hardware. Contract runs until 31 March 2027, starting ASAP.
WHAT WE’RE LOOKING FOR
Experience
- Hands-on engineering background in computer vision, robotics, edge AI, or industrial IoT — with demonstrable production deployments in real-world environments
- Fluent English — written and spoken, at both customer-facing and executive presentation level
- Available and willing to travel across Europe approximately 50% of the time
- Based within the European Economic Area (EEA)
Computer Vision
- Production-grade experience with OpenCV, object detection frameworks, and video analytics pipelines
- Model development and optimisation for inspection, tracking, anomaly detection, and real-time inference
Machine Learning
- Hands-on model development with PyTorch and/or TensorFlow for vision and perception tasks
- Understanding of model lifecycle management and operational performance monitoring
Edge AI & Embedded ML
- Deployment and optimisation of AI workloads on NVIDIA Jetson platforms
- Experience with TensorRT for inference optimisation and ONNX for model portability
- Edge-to-cloud architecture design for scalable AI operations
Robotics & Autonomous Systems
- Development and integration using ROS / ROS2 for real-world industrial applications
- SLAM for autonomous navigation and localisation
- Sensor-driven AI for autonomous perception and decision-making
Industrial IoT & Sensor Integration
- Integration of cameras, LiDAR, telemetry, and PLCs in operational environments
- Sensor fusion and real-time data acquisition for edge-to-cloud pipelines
- Familiarity with industrial automation protocols: OPC-UA, MQTT, SCADA
Programming
- Strong proficiency in Python and C++ for AI system development and optimisation
Customer & Delivery Skills
- Ability to lead requirements gathering, technical discovery workshops, PoC execution, and executive presentations
- Comfortable translating business challenges into AI solution designs and articulating business value to non-technical stakeholders
NICE TO HAVE
- Sector experience in manufacturing, steel, mining, energy, or industrial automation
- Experience with robotic vision systems, drones, or autonomous inspection platforms
- Digital Twins and simulation — NVIDIA Omniverse, Isaac Sim
- MLOps practices and tooling: Docker, Kubernetes, Azure AI
- Background in drone operations or autonomous monitoring systems in industrial environments
Originally posted on Himalayas
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