Computer Vision Engineer (4+ Years Experience)

Computer Vision Engineer Experience 4+ years of hands-on experience in Computer Vision, AI/ML model development, and production deployment Role Overview We are looking for a skilled Computer Vision Engineer who can design, develop, deploy, and lead real-world AI solutions. The role involves building image/video analytics systems, integrating LLM-powered components, and taking ownership of end-to-end deployment across cloud, on-prem, and edge environments. The ideal candidate should also demonstrate technical leadership and mentoring capabilities. Key Responsibilities Design, develop, and deploy computer vision solutions for image and video data Implement, fine-tune, and optimize object detection models (YOLO, SSD, Faster R-CNN, etc.) Build real-time inference pipelines with low latency and high reliability Collaborate with backend teams to expose models via APIs and services Own production deployment across cloud, on-prem, and edge devices Optimize models for performance, scalability, and cost efficiency Lead technical discussions, guide the team, and review code/model designs Maintain documentation for models, deployments, and system architecture Required Technical Skills Computer Vision & AI Strong fundamentals in Computer Vision and Deep Learning Hands-on experience with object detection, tracking, and video analytics Proficiency with OpenCV for image and video processing LLM & Generative AI Experience working with Large Language Models (LLMs) Knowledge of integrating CV outputs with LLMs (multimodal pipelines, RAG, AI agents, etc.) Familiarity with LLM APIs, prompt engineering, and inference optimization Understanding of real-world LLM deployment constraints (latency, cost, scaling) Programming & Frameworks Strong proficiency in Python Experience with PyTorch or TensorFlow Familiarity with dataset annotation, versioning, and experiment tracking Deployment & Infrastructure Proven experience deploying AI models into production environments Strong understanding of GPU-based inference and acceleration Experience with edge AI devices (e.g., NVIDIA Jetson or similar) Knowledge of model optimization tools (ONNX, TensorRT, quantization, pruning) Experience with Docker and containerized deployments Exposure to CI/CD pipelines for ML or MLOps workflows Education Bachelors or Masters degree in Computer Science, AI/ML, Electronics, or related fields .