Edge Deployment Engineer (DevOps Edge Infrastructure) R1012537

Georgetown, DE - USA

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

Edge Deployment Engineer (DevOps / Edge Infrastructure)

Georgetown / Lexington KY - On-site only (Local candidates required)

12 Months

Overview

We are seeking an Edge Deployment Engineer to support the deployment of pre-built pre-trained machine learning solutions onto customer edge devices in a manufacturing environment. This role is not focused on building or training ML models. Instead it centers on deploying packaging and operationalizing existing services close to production systems and optimizing them for performance reliability and ease of deployment.

This is a client-facing hands-on role working directly on-site with customer teams.

Key Responsibilities

Deploy and operate pre-trained ML models and supporting services on customer edge devices
Design and implement deployment scaffolding to package customer-provided services
Support edge infrastructure design including:
- Hardware recommendations
- Capacity planning
- Performance optimization
Standardize and streamline deployment processes across customer environments
Integrate data from cameras sensors and existing factory systems using established interfaces
Collaborate directly with client teams on-site to troubleshoot and improve deployments
Ensure solutions are reliable maintainable and easy to deploy at scale

Required Qualifications (Must-Haves)

Strong DevOps engineering background
Experience deploying services to edge or on-prem environments
Proficiency in Python for development and deployment support
Experience with containers and service packaging
Ability to perform light development work in addition to DevOps tasks
Comfortable working in manufacturing or industrial environments
Strong communication skills; client-facing experience required
Local to Georgetown / Lexington area

Preferred (Nice-to-Have)

Familiarity with ML / MLOps concepts (no model development required)
Exposure to PLC MES or factory systems at a high level
Experience working with industrial cameras sensors or edge compute devices
Background in system integration or deployment engineering

Not Required

Building or training ML models
Developing ML pipelines
Writing low-level device protocols
Controls engineering or PLC programming
Deep electronics mechanical or semiconductor domain experience