AI Operations Specialist
Role Overview
Junior-level role supporting day-to-day physical AI operations, from data collection and model-training support to edge deployment and system maintenance.
Combines hands-on technical execution with creative prototyping, working with vendors and engineering teams across the physical AI solution lifecycle.
Key Skills for This Role
Full Job Posting
Role overview
Junior-level role supporting day-to-day physical AI operations, from data collection and model-training support to edge deployment and system maintenance.
Combines hands-on technical execution with creative prototyping, working with vendors and engineering teams across the physical AI solution lifecycle.
Responsibilities
- Coordinate data collection, annotation, and preprocessing for computer vision, NLP, and robotics model training.
- Install, set up, and calibrate AI-enabled billboards, edge compute nodes, camera networks, sensors, and robotic assets.
- Monitor deployed-system uptime and health, perform first-line troubleshooting, and escalate bugs to engineering.
- Coordinate vendor deployments and integration tests; help resolve hardware-software conflicts.
- Propose real-world physical AI use cases and build, test, and demonstrate rapid prototypes.
- Follow security, privacy, and Responsible AI guidelines for operations, data gathering, and device management.
- Log deployments, system configurations, and incidents to support continuous product improvement.
Qualifications and experience
- 0–2 years in technical operations, software or hardware deployments, or robotics.
- Fundamental understanding of machine learning pipelines, data collection, model training, and edge inference.
- Familiarity with Linux, command-line interfaces, and shell scripting; basic programming in Python or C++ is requested.
- Mechanical and electronic aptitude to safely assemble, connect, and configure physical IT or robotic components.
- Bachelor's degree in Computer Science or Electrical Engineering.
Preferred experience and competencies
- Hands-on physical systems, edge hardware such as Raspberry Pi, NVIDIA Jetson, or Arduino, sensors, and basic electronics experience is highly preferred.
- Data gathering, model labeling tools, or manual system testing experience is a strong plus.
- Competencies listed include agility, AI fluency, data governance, integration and automation management, IT service management, operations management, resilience, and quality.
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