Principal AI Engineer

Role overview

AI, software, and architecture

• Defines software requirements from system-level requirements and translates them into robust architectures and designs.
• Leads architecture, design, development, integration, verification, and deployment of AI and computer vision software.
• Develops high-performance video analytics and AI software in Python and C++ for edge and embedded platforms.
• Leads object detection, tracking, recognition, classification, and other computer vision algorithm development.
• Develops AI/ML algorithms and selects architectures based on application requirements and constraints.
• Designs scalable architectures, system decomposition, interfaces, and integration strategies across engineering teams.

Integration, quality, and technical leadership

• Resolves complex software, AI, computer vision, and embedded-system issues on PC and edge devices.
• Profiles and optimizes CPU, GPU, memory bandwidth, I/O, and other resources to resolve performance bottlenecks.
• Leads integration across AI, embedded, hardware, systems, and test components.
• Defines coding standards, code review processes, testing methodologies, and quality gates.
• Leads and mentors technical teams and projects, influencing teams without direct authority.
• Plans verification activities including testing, analysis, inspections, and reviews; follows V-model and applicable quality, safety, and configuration processes.
• Contributes to programme milestones and formal reviews, including SDR, PDR, CDR, and TRR.
• Supports the product lifecycle through production, certification, deployment, testing, integration, and field support.

Basic qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Engineering, or a related technical discipline.
• 12+ years in software engineering, AI/ML engineering, computer vision, or a closely related field; at least four years in a principal, lead, architect, or equivalent technical leadership role.
• Strong hands-on Python and C/C++ programming; strong understanding of data science, machine learning, deep learning, and computer vision.
• Extensive computer vision and video analytics experience, including deep learning architectures and their mathematical foundations, capabilities, failure modes, and trade-offs.
• Experience with PyTorch and/or TensorFlow and designing AI/ML algorithms and architectures for system requirements.
• Experience taking complex AI/software solutions from concept through development, integration, verification, and production or field deployment.
• Strong knowledge of software architecture, design patterns, development methods, testing strategies, system decomposition, interfaces, integration, and lifecycle management.
• Ability to diagnose complex software and performance issues across embedded and/or PC platforms and lead technical teams or major workstreams.
• Strong analytical, problem-solving, communication, and decision-making skills; able to work independently and deliver amid changing priorities and demanding schedules.
• Willingness to travel and provide field support during testing and deployment when required.

Preferred qualifications

• Linux systems engineering, shell scripting, real-time Linux, determinism analysis, and latency characterization experience is preferred.
• Experience with NVIDIA Jetson, ROS 2, DDS, and real-time middleware optimization is preferred.
• Communication protocol experience, including MAVLink, and embedded or real-time edge software experience is preferred.
• Avionics, aerospace, defense, military, mission-critical, or safety-critical systems experience is preferred.
• Familiarity with DO-178B, ARP4754, MIL-STD-498, IEEE/EIA 12207, V-model lifecycles, and formal engineering reviews is preferred.
• Experience optimizing AI workloads for constrained compute, GPU, memory, power, latency, and real-time conditions is preferred.
• Experience establishing engineering governance and working across AI/ML, embedded, systems, hardware, verification, and programme management is preferred.

Work requirements

• The role may require travel and field support during testing and deployment.

Language

• English proficiency is required for all roles unless otherwise stated.

About EDGE