Computer Vision Engineer

Computer Vision Engineer

Job Summary

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 (e.g., YOLO, SSD, Faster R-CNN)
• 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

Requirements

Experience

4+ years of hands-on experience in Computer Vision, AI/ML model development, and production deployment

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 (e.g., multimodal pipelines, RAG, AI agents)
• Familiarity with LLM APIs, prompt engineering, and inference optimization
• Understanding of real-world LLM deployment constraints (e.g., 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 (e.g., ONNX, TensorRT, quantization, pruning)
• Experience with Docker and containerized deployments
• Exposure to CI/CD pipelines for ML or MLOps workflows

Education

Bachelor's or Master's degree in Computer Science, AI/ML, Electronics, or related fields