Remote Computer Vision Engineer (Edge AI and MLOps)

HCLTech; Asheville (NC); On-site; USD 87,000 - 172,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) retirement plan

10 days paid time off per year

10 paid holidays per year

Employee assistance program

Job summary

HCLTech is looking for a highly talented and self- motivated Computer Vision Engineer (AI POD) to join it in advancing the technological world through innovation and creativity. Location: Asheville, NC (Remote, require travel as per need).

Design robust computer vision solutions for assembly and defect inspection, and deploy deep learning models on edge devices and GPUs. Azure certification and frameworks PyTorch, TensorFlow, ONNX are required, plus FastAPI/Flask for REST services.

Qualifications

• Azure certification required.
• Proficiency with PyTorch, TensorFlow and ONNX.

Responsibilities

• Design robust computer vision solutions for assembly and defect inspection.
• Develop image processing and computer vision algorithms for detection, segmentation, OCR, and anomaly detection.
• Build, train, optimize, and deploy deep learning models for industrial applications.
• Design camera, lens, and lighting setups for inspection systems.
• Deploy AI models on edge devices and GPUs with optimization frameworks.
• Implement MLOps pipelines for model training, monitoring, and lifecycle management.
• Develop REST APIs and microservices to expose computer vision capabilities.
• Collaborate with manufacturing, automation, and software engineering teams.

Skills

Edge AI

API Development

Collaboration

Tools

PyTorch

TensorFlow

ONNX

NVIDIA Jetson

TensorRT

DeepStream

OpenVINO

FastAPI

Flask

REST Services

Job description

HCLTech is looking for a highly talented and self- motivated Computer Vision Engineer (AI POD) to join it in advancing the technological world through innovation and creativity. Location: Asheville, NC (Remote, require travel as per need).

Design robust computer vision solutions for assembly and defect inspection, and deploy deep learning models on edge devices and GPUs. Azure certification and frameworks PyTorch, TensorFlow, ONNX are required, plus FastAPI/Flask for REST services.