Computer Vision Engineer

Computer Vision Engineer

Computer Vision Engineer
Experience
2+ 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
Bachelor’s or Master’s degree in Computer Science, AI/ML, Electronics, or related fields