AI/ML, Computer Vision & Edge Infrastructure Engineer

Location: Dehradun, Uttarakhand

Work Mode: Full-time, In office

Company: EyeCU India

Website: https://eyecu.in

Company Description

EyeCU India transforms existing CCTV camera infrastructure into proactive, real-time visual intelligence for industrial sites and commercial vessels. Powered by edge AI and computer vision, the platform continuously monitors high-risk zones, delivering life safety, exclusion zone, PPE compliance and industrial security alerts in seconds.EyeCU solutions are designed for commercial maritime, offshore rigs, ports, terminals, heavy manufacturing, logistics hubs and energy facilities. Our edge-based architecture enables local AI processing and critical safety alerts even in environments with limited or unreliable internet connectivity.

Role Description

EyeCU is looking for a versatile AI/ML, Computer Vision & Edge Infrastructure Engineer to take ownership of the development, deployment and technical reliability of its AI-powered video intelligence platform.This is a hands-on engineering role combining computer vision, machine learning, server infrastructure, GPU configuration, edge computing and field deployment.The candidate will be responsible for developing and optimising AI models for real-time detection of fire, smoke, PPE violations, unsafe behaviour, exclusion zone breaches and operational anomalies. They will also configure Linux and GPU servers, integrate existing CCTV/IP camera and NVR systems, deploy containerised applications and ensure reliable operation across customer facilities and vessels.The role involves taking EyeCU from AI model development to actual production deployment, including remote monitoring, troubleshooting and system maintenance.

- Key Responsibilities1. AI, Machine Learning & Computer VisionDevelop, train, test and optimise computer vision and deep learning models.
- Work with PyTorch, TensorFlow, OpenCV and modern object detection architectures.




- Develop real-time video analytics for safety, security and operational monitoring.
- Improve detection accuracy, inference speed and false-positive performance.
- Optimise models for NVIDIA GPU and edge computing environments.
- Support data collection, annotation, model evaluation and continuous improvement.
- 2. Server Infrastructure & GPU ConfigurationInstall, configure and maintain Linux-based servers.
- Configure NVIDIA GPUs, CUDA, drivers and associated libraries.
- Set up Docker-based applications and containerised environments.
- Manage databases, storage, networking, backups and system security.
- Configure cloud and on-premises infrastructure.
- Implement remote monitoring, software updates and troubleshooting.
- 3. Edge Computing & System DeploymentInstall and commission edge computing servers at industrial facilities and onboard vessels.
- Integrate existing CCTV cameras, IP cameras and NVR systems.
- Configure RTSP streams, ONVIF protocols and video processing pipelines.
- Deploy AI applications for local processing and real-time alerts.
- Ensure uninterrupted operation during internet outages.
- Conduct system testing, commissioning, documentation and customer handover.
- 4. Maintenance & Technical SupportProvide remote technical support for deployed systems.
- Diagnose server, network, camera and AI application issues.
- Manage software upgrades, model updates and system performance.
- Minimise the need for physical engineering visits to customer locations.
- Coordinate with product, software and deployment teams.




- Qualifications & Technical SkillsBachelor's or Master's degree in Computer Science, AI/ML, Electrical Engineering, Data Science or a related field, or equivalent practical experience.
- Strong programming skills in Python; C++ is an advantage.
- Hands-on experience with PyTorch, TensorFlow, OpenCV and computer vision.
- Knowledge of neural networks, deep learning, pattern recognition and video analytics.
- Strong Linux administration skills (Ubuntu/Red Hat).
- Experience with NVIDIA GPUs, CUDA and GPU-based inference.
- Working knowledge of Docker; Kubernetes is an advantage.
- Understanding of networking, TCP/IP, VLAN, VPN and server administration.
- Experience with RTSP, ONVIF, CCTV/IP cameras and NVR integration
- Familiarity with PostgreSQL, Redis, cloud infrastructure and CI/CD.
- Experience deploying AI applications on edge devices or industrial servers.

Experience

Ideally, 2–6 years of relevant hands-on experience in AI/ML, computer vision, server infrastructure or edge computing, with demonstrable experience in production deployment.

Candidates with practical experience across multiple areas are preferred over those with purely academic or research-based exposure.

Experience in maritime, offshore, oil & gas, industrial automation or safety-critical environments will be an advantage.

What We Are Looking For

We need an engineer who can independently take an AI application from development to deployment: train or optimise the model, configure the GPU server, connect the cameras, install the software, commission the system and maintain it remotely.The ideal candidate should be comfortable working with both software and physical infrastructure, troubleshooting real-world problems and taking ownership of technical delivery.

- This is not a purely software development or AI research position. It requires practical engineering capability across AI, servers, networking, CCTV integration and edge deployment.

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