Job Opportunities

Computer Vision Engineer (4+ Years Experience)

Role Overview
We are looking for a skilled Computer Vision Engineer with 4+ years of hands-on experience in Computer Vision, AI/ML model development, and production deployment; 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
1. Design, develop, and deploy computer vision solutions for image and video data
2. Implement, fine-tune, and optimize object detection models (YOLO, SSD, Faster R-CNN, etc.)
3. Build real-time inference pipelines with low latency and high reliability
4. Collaborate with backend teams to expose models via APIs and services
5. Own production deployment across cloud, on-prem, and edge devices
6. Optimize models for performance, scalability, and cost efficiency
7. Lead technical discussions, guide the team, and review code/model designs
8. Maintain documentation for models, deployments, and system architecture

Required Technical Skills
1. Computer Vision & AI
2. Strong fundamentals in Computer Vision and Deep Learning
3. Hands-on experience with object detection, tracking, and video analytics
4. Proficiency with OpenCV for image and video processing
5. LLM & Generative AI
6. Experience working with Large Language Models (LLMs)
7. Knowledge of integrating CV outputs with LLMs (multimodal pipelines, RAG, AI agents, etc.)
8. Familiarity with LLM APIs, prompt engineering, and inference optimization
9. Understanding of real-world LLM deployment constraints (latency, cost, scaling)
10. Programming & Frameworks
11. Strong proficiency in Python
12. Experience with PyTorch or TensorFlow
13. Familiarity with dataset annotation, versioning, and experiment tracking

Deployment & Infrastructure
1. Proven experience deploying AI models into production environments
2. Strong understanding of GPU-based inference and acceleration
3. Experience with edge AI devices (e.g., NVIDIA Jetson or similar)
4. Knowledge of model optimization tools (ONNX, TensorRT, quantization, pruning)
5. Experience with Docker and containerized deployments
6. Exposure to CI/CD pipelines for ML or MLOps workflows

Education
1. Bachelor’s or Master’s degree in Computer Science, AI/ML, Electronics, or related fields