Computer Vision Engineer

TITLE: Computer Vision Engineer LOCATION: Gurugram/Bangalore WFO. Build the intelligence behind real-world video. At Awiros, we’re building an operating system for Computer Vision - a platform that enables developers and enterprises to build, deploy, and scale AI-powered video applications without having to reinvent the infrastructure underneath. Founded in 2015 and backed by Series A funding (~$7M raised), we work at the intersection of Deep Learning, Computer Vision, distributed systems, and real-time video analytics. Our technology powers applications across detection, recognition, tracking, safety, security, and operational intelligence. And this isn’t Computer Vision in a notebook. Our models run on real-world video, at scale, across edge and cloud environments — where accuracy, latency, robustness, and compute efficiency all matter. That’s where you come in. THE ROLE: We’re looking for Computer Vision Engineers who enjoy turning challenging perception problems into reliable, production-ready solutions. You’ll work across the Computer Vision and Deep Learning stack - building and fine-tuning models, evaluating performance, optimizing inference, & integrating solutions into real-world video analytics applications. The challenge isn’t simply getting a model to work. It’s making it work reliably in the real world — across different environments, hardware configurations, and deployment conditions, while balancing accuracy, latency, memory, throughput, and reliability. You’ll work closely with our Computer Vision, Platform, Product, and C++ engineering teams to take solutions from implementation through optimization and into production. WHAT YOU'LL WORK ON: Design, build, and optimize vision algorithms and deep learning models for real-time video analytics. Develop solutions across object detection, tracking, re-identification, pose estimation, recognition, segmentation, and anomaly detection. Select, adapt, and fine-tune deep learning architectures based on the requirements of a specific problem and deployment environment. Work with real-world datasets to train, evaluate, benchmark, and improve model performance. Analyze model and inference bottlenecks across accuracy, latency, memory, throughput, and compute utilization. Optimize models for deployment using techniques such as quantization, pruning, model simplification, and architecture optimization. Collaborate with C++ and systems engineers to integrate models into production pipelines and deliver validated model artifacts such as ONNX models. Debug challenging detection and perception failures, identify root causes, and build robust solutions. Develop evaluation workflows and performance benchmarks to measure and continuously improve our Computer Vision systems. Evaluate new Computer Vision techniques and technologies based on their practical applicability and potential product impact. Take ownership of problems from…