Description

Lead development and deployment of ML model interfaces for real-time fault detection in laser welding applications.

This role is on-site.

Responsibilities

• Configure and integrate Ignition Perspective, HiveMQ MQTT brokers, and PostgreSQL databases across plant and DMZ networks.
• Manage data pipelines, dashboards, and historian setups to visualize machine metrics and inference results.
• Oversee edge deployments using NVIDIA hardware, Docker containers, and AWS IoT Greengrass.
• Build and maintain secure, scalable MQTT bridges between cloud and plant networks.
• Implement automated CI/CD pipelines, infrastructure provisioning with Terraform, and DevSecOps practices.

Required Skills

• 5+ years of experience in industrial system integration including PLCs, industrial PCs, and OT/IT network architectures.
• Proficiency with Ignition (Perspective & Edge) and HiveMQ MQTT.
• Experience with machine learning deployment and real-time inference in manufacturing environments.
• Hands-on experience with Docker containerization and edge computing.
• Strong background in data pipeline design, sensor data processing, and image/media handling.
• Technical proficiency with AWS (IoT Greengrass, IoT Core, S3, DataSync/Storage Gateway, Databricks).
• Infrastructure automation using Terraform and AWS CI/CD tools (CodeBuild, ECR).
• PostgreSQL database management.

Preferred Skills

• Experience with GPU accelerated ML models and deployments to NVIDIA hardware.
• Knowledge of AWS SageMaker and media metadata management.
• Experience with laser welding or automated manufacturing processes.

Key Skills

Education

Any Gradute