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Metro location and mid-level experience but niche video-analytics deployment skills limit applicant pool.
Core Linux, DevOps skills transfer well but camera/ONVIF and edge deployment domain knowledge reduces portability.
Explicit 2–5 years plus mandatory Linux, networking, containerization and camera/integration skills increase filtering strictness.
Own end-to-end deployment of AI-driven video analytics software including hardware sizing, OS setup, container installation/configuration, and system integration with cameras, NVRs, VMS and customer IT systems.
Automate deployment using Docker, shell/Ansible scripts, manage remote updates/rollback, and maintain system health monitoring and cybersecurity hardening.
Provide Level 2 post-deployment field support and troubleshooting, involving 30–50% travel to customer sites across India.
2–5 years experience deploying software on Linux servers or embedded/edge devices.
Diploma or B.Tech in Computer Science, Electronics, IT or equivalent.
Strong Linux administration skills (Ubuntu) and networking fundamentals including IP addressing, VLANs, port forwarding, firewalls, VPN, PoE switches.
Knowledge of IP cameras, NVR/DVR, RTSP/ONVIF protocols, video codecs, bandwidth/storage calculations; Bash and/or Python scripting for automation and troubleshooting.
Experienced in deploying complex AI or video analytics software across multi-site environments with customer-facing responsibilities.
Comfortable working across hardware (edge devices, GPUs), software stacks, networking, and video integration components in surveillance contexts.
Operates well within deployment teams bridging AI software, QA, and IT operations to meet project timelines and specifications.