Senior Engineering Lead - Edge AI Systems
Location: Kochi (on-site preferred)
Reports to: Head of Engineering / CTO
About the Role
We're looking for a hands-on Senior Engineering Lead to own delivery of Flagman, our edge AI computer vision platform deployed in industrial environments. You'll lead a team of 6 engineers spanning embedded systems, ML/computer vision, and cloud infrastructure — while staying in the code yourself. This is a player-coach role: you're accountable for timelines and release quality, and technically credible enough to unblock the team when it matters.
What you will do
- Own project planning, sprint execution, and release timelines across embedded, ML, and infrastructure workstreams; flag risks and slips early with credible mitigation plans
- Break ambiguous product requirements into scoped, estimable work and manage cross-track dependencies
- Review and contribute code across the stack: Python ML pipelines (detection, tracking, edge inference), embedded Linux in C++ (GStreamer, GPIO, systemd), and backend services (Go, PostgreSQL)
- Lead design reviews for high-stakes changes and personally take on critical-path tasks when the team is stretched
- Debug production issues on real hardware in the field
- Manage, mentor, and grow the team: 1:1s, goals, feedback, performance conversations, and hiring
- Coordinate with field engineering on deployments and customer escalations
What we are looking for:
Must-have
- 8+ years of engineering experience, with 2+ years leading a team
- Track record of shipping hardware-adjacent or embedded products on committed timelines
- Strong hands-on skills in at least two of: embedded Linux with C++ exposure, ML inference/deployment (PyTorch, ONNX), backend services (Go or Python, PostgreSQL) — and able to review code across all three
- Confident with AI-assisted coding workflows, using them to accelerate delivery without compromising quality
- Clear written communication: status updates, design docs, postmortems
Strong plus
- Edge AI / computer vision product experience (camera systems, video pipelines)
- Industrial automation exposure: PLCs, industrial protocols, sensors
- CI/CD for embedded targets and on-device testing
Success Looks Like
Releases ship on committed dates or slips are flagged early; field-reported issues turn around fast; the team retains, grows, and owns their areas clearly.
