Computer Vision Engineer - Edge Deployment

Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystemcomprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 marketsto create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.

Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.

About Job

Role Overview:

As a Computer Vision Engineer with a focus on edge deployment, you will design, develop, and optimize computer vision algorithms that can run efficiently on edge devices. You will collaborate closely with cross-functional teams to deliver solutions that balance computational performance, model accuracy, and resource constraints.

Key Responsibilities:

Algorithm Development: Develop and implement computer vision and deep learning algorithms for various tasks (e.g., object detection, tracking, segmentation) that are optimized for edge devices. Model Optimization: Utilize techniques such as quantization, pruning, and model compression to make deep learning models lightweight and efficient for edge deployment. Edge Deployment: Deploy and monitor CV models on edge devices, ensuring low latency and high reliability in real-time applications. Performance Analysis: Evaluate and improve the performance of algorithms on edge platforms, including latency, power consumption, and memory utilization. Hardware Integration: Work with various edge hardware, and frameworks (e.g., TensorRT) for deployment. Collaboration: Work closely with hardware engineers, software developers, and data scientists to align on system requirements and optimize end-to-end performance.

Qualifications:

• Bachelor's or Master's in Computer Science, Electrical Engineering, or related field (or equivalent practical experience).
• 3/5/7+ years of experience in computer vision or deep learning, with a focus on edge deployment.
• Strong proficiency in Python and C/C++ and familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Hands-on experience with edge deployment frameworks and tools (e.g., TensorRT, OpenVINO, CoreML).
• Proficient in model optimization techniques for edge devices, including quantization, pruning, and knowledge distillation.
• Understanding of edge hardware and experience with platforms like NVIDIA Jetson, Google Coral, or mobile chipsets.
• Familiarity with performance profiling tools and techniques for analyzing and optimizing model runtime on edge devices.
• Excellent problem-solving skills, teamwork, and communication abilities.

Preferred Skills:

• Experience in real-time application development and edge AI pipelines.
• Knowledge of azure based services is plus
• Familiarity with IoT protocols and system security for edge AI applications.
• Experience and hands-on in VLM is huge plus