Computer Vision Engineer

Difinity Digital; Ernakulam; On-site; INR 1,200,000 - 2,200,000; Full time

Job summary

Difinity Digital is seeking a skilled Computer Vision Engineer to design, develop, deploy, and lead real-world AI solutions. You will build image and video analytics systems, integrate LLM-powered components, and own end-to-end deployment across cloud, on-prem, and edge environments.

The ideal candidate will have 4+ years of hands-on experience in CV, AI/ML model development, and production deployment, with leadership capabilities to mentor teammates and drive architectural decisions.

Qualifications

• 4+ years hands-on experience in Computer Vision, AI/ML model development, and production deployment.
• Experience designing and deploying image/video analytics systems and leading end-to-end deployments across cloud, on-prem and edge.
• Strong fundamentals in CV and Deep Learning, with hands-on object detection, tracking, and video analytics.

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

Skills

Python

Computer Vision

Deep Learning

Object detection

Video analytics

LLM integration

Edge AI

Education

Bachelor's or Master's degree in CS/AI/EE

Tools

OpenCV

PyTorch

TensorFlow

ONNX

Docker

Job description

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