OT Artificial Intelligence Edge Integration Engineer (ACE AI Project)

OT Artificial Intelligence | Edge Integration Engineer

Position Overview

We are seeking an OT Artificial Intelligence Edge Integration Engineer to design, integrate, deploy, and support AI-enabled edge solutions in industrial environments. This role connects sensors, cameras, automation systems, industrial data sources, edge computing platforms, and AI workloads into secure, reliable, and supportable solutions operating close to industrial assets.

The position provides hands-on engineering leadership for integrating operational technology (OT) and AI capabilities at the industrial edge. The engineer develops reusable connectivity, computing, deployment, and support patterns that enable machine learning, computer vision, generative AI, and agentic workloads to operate safely across constrained, intermittently connected, and disconnected field environments.

The successful candidate will collaborate with OT specialists, AI and data engineers, solution architects, cybersecurity professionals, infrastructure teams, vendors, and operations personnel to deliver field-ready AI solutions. The focus is on dependable integration with existing industrial systems while maintaining safety, cybersecurity, availability, performance, maintainability, and clear operational ownership.

High-Level Job Description / Scope

This role focuses on edge integration engineering for industrial AI solutions. Responsibilities include industrial connectivity, interface design, edge computing, device onboarding, workload deployment, local data processing, fleet management, observability, remote support, and lifecycle management.

The engineer integrates AI solutions with cameras, sensors, historians, control and automation platforms, robotics systems, and authorized enterprise or cloud services while respecting industrial security boundaries and operational constraints.

Key Responsibilities

1. Design and implement end-to-end integration patterns for industrial AI solutions spanning field devices, industrial networks, edge computing, and centralized services.

2. Integrate cameras, sensors, gateways, historians, automation systems, robotics platforms, and other industrial data sources with edge AI workloads.

3. Define and implement interfaces using industrial and application protocols such as OPC UA, MQTT, Modbus TCP, REST APIs, and event-streaming technologies.

4. Size, configure, commission, and validate edge computing hardware, operating systems, containers, accelerators, storage, and network connectivity for AI workloads.

5. Package and deploy AI inference services, supporting software, and configurations to edge devices through repeatable and controlled delivery processes.

6. Engineer local buffering, store-and-forward, synchronization, and recovery mechanisms for disconnected or intermittently connected operations.

7. Implement secure device identity, certificate and secrets management, access controls, network segmentation, and encrypted communications across industrial network zones.

8. Establish fleet-management practices for device onboarding, inventory, configuration, software distribution, health monitoring, patching, and retirement.

9. Implement observability for edge devices, connectivity, data flows, AI services, hardware accelerators, and integration points, including actionable alerting.

10. Develop commissioning plans and test solutions under representative load, latency, bandwidth, environmental, failure, and recovery conditions.

11. Troubleshoot cross-layer issues involving devices, protocols, networks, data, containers, drivers, models, and downstream industrial workflows.

12. Produce interface specifications, network and data-flow diagrams, configuration baselines, bills of material, commissioning records, runbooks, and support procedures.

13. Collaborate with AI, industrial technology, architecture, cybersecurity, infrastructure, vendor, and operations teams to transition edge AI solutions from pilot deployments to supported production environments.

Required Qualifications

1. Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, Automation Engineering, or a related field, or equivalent practical experience.

2. Five or more years of experience integrating and supporting industrial edge, IoT, automation, or distributed computing solutions.

3. Hands-on experience with industrial protocols and interfaces such as OPC UA, MQTT, Modbus TCP, REST APIs, or equivalent technologies.

4. Experience configuring and supporting Linux or Windows edge systems, containers, device drivers, local storage, and network connectivity.

5. Experience deploying and supporting workloads across edge, on-premises, and cloud-connected environments.

6. Experience with industrial networks, firewalls, routing, name resolution, time synchronization, certificates, and secure remote support.

7. Experience implementing device onboarding, configuration, monitoring, patching, software updates, and lifecycle management.

8. Experience designing for disconnected operation, intermittent connectivity, store-and-forward data movement, and safe recovery.

9. Understanding of OT cybersecurity, Purdue model concepts, IEC 62443 zones and conduits, least-privilege principles, and change-control requirements.

10. Strong scripting and automation skills using Python, PowerShell, shell scripting, or equivalent tools.

11. Demonstrated ownership of field deployments, including commissioning, troubleshooting, documentation, handover, and production support.

Preferred Qualifications

1. Master's degree in Computer Engineering, Electrical Engineering, Automation Engineering, or a related field.

2. Experience with Azure IoT Operations, Azure Arc, Azure Local, Kubernetes, K3s, or equivalent industrial edge platforms.

3. Experience integrating computer vision systems, fixed cameras, thermal imaging, drones, robots, or autonomous inspection platforms.

4. Experience deploying GPU-, NPU-, or accelerator-enabled AI inference at the industrial edge.

5. Experience with over-the-air updates, edge orchestration, fleet management, and signed deployment artifacts.

6. Experience with historians, distributed control systems, programmable logic controllers, supervisory control and data acquisition systems, or industrial data platforms.

7. Experience with OpenTelemetry, Prometheus, Grafana, or equivalent edge observability technologies.

8. Experience selecting industrial-grade computing, networking, power, and enclosure components for harsh or hazardous environments.

9. Familiarity with machine learning and computer vision inference lifecycles, model packaging, performance monitoring, and rollback procedures.

10. Experience in upstream, midstream, or downstream industrial environments and the ability to work directly with asset and operations personnel.

Critical Selection Criteria

1. Edge Integration Capability: Demonstrated ability to integrate industrial devices, protocols, networks, computing platforms, and AI workloads into secure, supportable end-to-end solutions.

2. Operational Reliability: Ability to engineer for constrained connectivity, environmental conditions, controlled change, recovery, remote support, and dependable field operation.

3. OT, Security, and AI Understanding: Knowledge of industrial automation, OT cybersecurity, edge computing, and the runtime requirements of machine learning and computer vision solutions.

4. Field Execution and Collaboration: Ability to translate field requirements into practical integration designs and coordinate commissioning, troubleshooting, and handover with technical and operational stakeholders.