Computer Vision Engineer

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

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