Engineering Manager- Edge AI & Computer Vision

Key Responsibilities

Project Execution & Delivery

• Own the end-to-end delivery of software and hardware-integrated projects, including

large-scale computer vision and tracking systems (e.g., OCR systems, smart

warehouse, smart yard solutions). Set realistic timelines, manage sprints, and

ensure the team consistently meets its delivery commitments.

Hardware & Infrastructure Coordination

• Oversee validation and deployment of edge computing hardware, including high-

performance GPUs (NVIDIA, AMD), advanced processors (AMD Ryzen, Intel), and

specialized camera arrays.

Site Deployment Management

• Coordinate technical requirements for physical deployments, including Bill of

Materials (BOM) validation, networking infrastructure (e.g., Starlink, high-speed

networks), and compliance with environmental standards (NEMA-rated enclosures,

IP-rated components, thermal dissipation).

Technical Strategy & Guidance

• Participate in architecture discussions, code reviews, and technical trade-off

decisions. Collaborate on scaling centralized API management systems, optimizing

While you wont write production code daily, youll stay technically engaged.

Cross-Functional Collaboration

• Partner with Product, Design, QA, and technical leadership to translate business

requirements into clear technical action plans and ensure all solutions meet rigorous

industrial performance standards before production.

Team Leadership & Mentorship

• Manage and mentor a team of 5–10 engineers. Conduct 1-on-1s, guide career

development, remove blockers, and foster a culture of high performance,

psychological safety, and clear communication.

Process Optimization

• Know when to follow the process and when to adapt it to maximize throughput and

quality.

Risk & Roadblock Management

• Exceptional ability to troubleshoot on the fly, whether its a network connectivity

issue at a remote site or a model routing bottleneck in the cloud.

Skills & Requirements

• 3+ years of engineering management experience and 3+ years as a hands-on

• Experience deploying production-grade computer vision applications.

• Strong foundational knowledge of computer vision pipelines, edge computing

architecture, and networking.

• Ability to design and build applications, not just oversee them - this is a hands-on role.

• Exceptional written and verbal communication skills, including the ability to explain

technical blockers clearly to non-technical stakeholders.

• Outstanding problem-solving instincts and cross-functional collaboration skills - a

genuine curiosity to learn while tackling complex challenges.

• Working knowledge of REST APIs and web application development.

Good to Have

• Prior experience with containerized AI deployments on edge devices.

• Proven track record of shipping complex software projects and hardware-software

integrated products (IoT or Edge AI systems strongly preferred).

• Hands-on experience managing physical deployments in industrial or outdoor

environments, with an understanding of ruggedized hardware constraints.

• Familiarity with supply chain and logistics operations.

• Understanding of modern software architecture, cloud infrastructure (AWS/Azure),

CI/CD pipelines.