Lead Computer Vision & Edge AI Engineer

Location: Jaipur, Rajasthan

Work arrangement: Full-time, office-based

Experience: 5–10 years

Reporting to: CTO / CEO

HawkVision AI is a UK-headquartered computer vision company with its engineering and delivery centre in Jaipur, India. We build real-time video analytics that help industrial organisations improve workplace safety and operational performance using their existing CCTV infrastructure.

Our platform covers use cases including PPE detection, vehicle–pedestrian proximity monitoring, restricted-zone intrusion, person-down and fall detection, unsafe work-at-height, fire, smoke, spill and leak detection, and material-handling and behavioural safety analytics.

We have customers and pilots across India, the USA, the UK, Europe, the Middle East and Africa, and have been recognised by Verdantix as an innovative provider of video analytics for workplace safety. We are on our path to becoming the leading company in this space, working with Fortune 500 companies across the globe. HawkVision AI is ISO 27001 certified and SOC 1 compliant, and is currently working towards SOC 2 compliance.

We are looking for a hands-on Lead Computer Vision & Edge AI Engineer to own the complete lifecycle of our computer vision models — from understanding the safety problem and preparing datasets through training, validation and deployment of optimised inference pipelines across live multi-camera CCTV streams.

This is not a pure research or model-training role. You will also mentor our computer vision engineers and establish scalable development, testing and deployment practices.

•   Design, train and improve object-detection, segmentation, tracking and activity-recognition models for challenging industrial CCTV environments — poor lighting, occlusion, unusual camera angles, low resolution and long distances.

•   Solve small-object detection for PPE such as helmets, gloves and safety shoes, and build temporal models for person-down, falls and unsafe interactions.

•   Select model architectures that balance accuracy, speed and edge-computing constraints.

•   Own data-collection, annotation and dataset-quality standards, including labelling guidelines and balanced datasets for rare safety events.

•   Drive iterative improvement through hard-negative mining and active learning; identify dataset bias, leakage and overfitting.

•   Establish camera-wise and use-case-wise performance evaluation.

•   Convert and optimise models with ONNX and TensorRT; build DeepStream and GStreamer pipelines on NVIDIA RTX and Jetson edge systems.

•   Apply FP16/INT8 quantisation; optimise GPU memory, batching, decoding and multi-camera throughput; benchmark streams supported per GPU configuration.

•   Diagnose frame drops, latency and RTSP instability in production; work fluently with Docker and Linux.

•   Set precision, recall and false-alarm targets, and measure real incident-detection performance rather than headline model accuracy.

•   Analyse failures, build validation datasets that reflect real customer environments, define acceptance criteria for pilots and production, and automate regression testing.

•   Mentor computer vision engineers and annotation teams; review architectures, training code and deployment pipelines; set coding, documentation and experimentation standards.

•   Translate customer safety requirements into feasible solutions alongside product, delivery and customer teams; support technical discussions with enterprise customers; shape the long-term vision and edge-AI architecture of the platform.

•   5–10 years in machine learning or computer vision, including 3+ years deploying computer vision solutions in production.

•   Strong Python and PyTorch; YOLO or comparable detection frameworks; object detection, segmentation and multi-object tracking.

•   Hands-on experience with real CCTV/RTSP video streams and NVIDIA GPU edge inference.

•   NVIDIA DeepStream, TensorRT, ONNX and GStreamer; good working knowledge of Linux and Docker.

•   Proven optimisation of models for latency, throughput and GPU utilisation.

•   Strong grounding in precision, recall, F1, confusion matrices and false-alarm analysis.

•   Skilled at diagnosing data-quality, model-performance and production issues; writes production-quality, maintainable code.

•   Experience leading or mentoring engineers.

•   Domain experience in industrial safety, manufacturing, logistics, construction, mining or CCTV analytics.

•   PPE detection, behavioural safety analytics, person-down/fall or action-recognition models; small-object detection in heavily occluded scenes.

•   CUDA or C++ optimisation; deployments across 20+ simultaneous video streams; NVIDIA Triton Inference Server.

•   Model quantisation, pruning and knowledge distillation.

•   MLflow, Weights & Biases or similar; managing large-scale video datasets and annotation teams.

•   Integrating models with cloud applications and APIs.

•   Publications or patents in computer vision are welcome but not essential.

•   Hands-on with models, code and live camera streams; comfortable moving between research, engineering and production troubleshooting.

•   Able to explain why a model is failing and propose a practical solution.

•   Focused on customer outcomes and real incident-detection performance; balances accuracy with edge-computing constraints.

•   A strong mentor who thrives in a fast-growing startup and wants to build technology that keeps workers safe.

•   A senior technical leadership role for a high achiever — lead innovation on the core platform of a fast-growing AI computer vision startup with a significant customer base across the world.

•   Work and lead directly with the founders, with exposure to real industrial deployments across India, the USA, the UK and Europe and to Fortune 500 customers and senior industry leaders.

•   Competitive compensation, performance-linked incentives, and the opportunity for significant stock options with huge growth potential.

•   Relocation support for candidates moving to Jaipur, in a collaborative and entrepreneurial environment.

Please email to subhash.sharma@hawkvision.ai

•   Your CV or LinkedIn profile.

•   A brief description of two computer vision systems you personally helped deploy in production.

•   Your experience with DeepStream, TensorRT, GStreamer and multi-camera video analytics.

•   Your current location, notice period and compensation expectations.

GitHub profiles, technical publications or project portfolios are welcome.