Job description
The Role: Lead Engineering, Computer Vision, and QA teams to build and deploy DeepInspect, our edge AI-enabled solution for manufacturing quality checks. Own the full development lifecycle, ensuring 99.99% accuracy and sub-second latency on the factory floor. Focus on AI, embedded systems, and real-world manufacturing.
About SwitchOn: Series B deep tech startup eliminating manufacturing defects with AI-powered vision inspection systems (DeepInspect). Clients include Unilever, Diageo, Hyundai, and Bosch. 200+ global deployments across 40+ enterprise customers. Visit our website for more.
Key Responsibilities
Lead, mentor, and scale Application Development, Computer Vision, and QA teams. Architect interactive, AI-based applications on embedded hardware. Define HLD/LLD for scalable data processing and model deployment infrastructure. Partner with Product/Sales/Customer teams for critical product scaling. Implement scalable Over-The-Air (OTA) update architectures for global plants.
Champion engineering best practices, quality, and continuous improvement; drive hiring.
Requirements
7+ years of software engineering (2+ years in management). Proven leadership and mentoring skills. Deep understanding of computer science fundamentals (data structures, algorithms, OS). Strong Python application development and on-prem/air-gapped deployment experience. Experience building/maintaining RESTful APIs (FastAPI, Flask, Django).
Solid experience in data processing pipeline and system architecture (HLD & LLD) design. Familiarity with deep learning models, libraries, and Computer Vision applications. Strong understanding of deploying applications on edge systems. Excellent communication (written and verbal); experience with Linux and CI/CD/unit testing.
Why Join Us? Impact: Build critical, non-demo infrastructure for our Vision AI platform running live at major companies (Unilever, P&G, ITC, Maruti), inspecting millions of units daily. Growth: Scale globally with top deep-tech investors; architectural decisions will define the next decade. Technical Challenge: Unique problem leading a team shipping at the edge: embedded hardware, real-time inference, and OTA updates to air-gapped environments.
Rewards: Meaningful equity participation with potential for personal impact.