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

LLM and model routing workflows, and setting up developer budgets and policies.

While you won't write production code daily, you'll 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 it's 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

software or AI Engineer.

• 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.