OT Artificial Intelligence Application Developer (ACE AI Project)

OT Artificial Intelligence | Application Developer

Position Overview

We are seeking an OT Artificial Intelligence Application Developer to design, build, integrate, and support secure software applications that bring AI capabilities into industrial workflows. This role creates intuitive, reliable applications that connect operators, engineers, and field personnel with computer vision, predictive analytics, generative AI, agentic systems, and other industrial AI services.

The position provides hands-on application engineering and delivery support for industrial AI use cases. The developer creates reusable user-interface, API, integration, and workflow patterns that enable AI insights and recommendations to be consumed safely across web, mobile, edge, and authorized enterprise environments.

The successful candidate will collaborate with industrial technology specialists, product owners, user-experience designers, AI and data engineers, solution architects, cybersecurity professionals, and operations personnel to turn AI capabilities into practical operational solutions. The focus is on delivering applications that are intuitive, responsive, secure, observable, maintainable, and dependable under real-world field conditions.

High-Level Job Description / Scope

This role focuses on application development for industrial AI solutions. Responsibilities include user experience, front-end and back-end services, APIs, workflow automation, AI-service integration, authentication, authorization, testing, deployment, observability, and support.

The developer builds applications for operator decision support, inspection, asset monitoring, exception management, and field execution while accounting for industrial cybersecurity, connectivity, and availability constraints.

Key Responsibilities

1. Design and develop secure, responsive, and maintainable web, mobile, or edge applications that integrate industrial AI capabilities into operational workflows.

2. Build intuitive user experiences for AI-assisted inspection, asset monitoring, anomaly review, decision support, alert triage, and operational follow-up.

3. Develop back-end services and APIs that integrate applications with machine learning models, computer vision services, retrieval systems, AI agents, and industrial data platforms.

4. Integrate applications with historians, event streams, asset context, inspection systems, document repositories, and authorized enterprise services.

5. Implement human-in-the-loop workflows that clearly communicate AI confidence, limitations, supporting evidence, and required operator actions.

6. Engineer local caching, synchronization, graceful degradation, and recovery mechanisms for constrained, intermittently connected, or disconnected field environments.

7. Implement authentication, role-based access controls, secrets handling, secure communications, input validation, and audit logging in alignment with industrial cybersecurity requirements.

8. Develop automated unit, integration, security, accessibility, performance, and user-acceptance tests as part of the software delivery pipeline.

9. Instrument applications with logs, metrics, traces, and usage telemetry to support monitoring, diagnostics, and continuous improvement.

10. Package and deploy applications through controlled CI/CD processes across approved edge, on-premises, and cloud environments.

11. Troubleshoot cross-layer issues involving user interfaces, APIs, identity, data, AI services, networks, and downstream operational workflows.

12. Produce application architecture documentation, interface specifications, data-flow diagrams, support runbooks, release notes, and user documentation.

13. Contribute to an Agile product team and continuously improve reusable application patterns, components, and engineering practices for industrial AI solutions.

Required Qualifications

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

2. Five or more years of experience developing, deploying, and supporting production software applications.

3. Strong programming skills in JavaScript or TypeScript and at least one back-end language, such as C#, Python, or Java.

4. Hands-on experience with modern front-end frameworks such as React, Angular, or equivalent technologies, along with responsive user-interface design.

5. Experience developing RESTful or event-driven services, working with structured and unstructured data, and integrating third-party APIs.

6. Experience integrating applications with machine learning, computer vision, generative AI, or other intelligent services.

7. Experience with automated testing, source control, code review, CI/CD, containers, and production release practices.

8. Experience implementing authentication, authorization, secure coding practices, secrets management, and application telemetry.

9. Understanding of industrial cybersecurity, network segmentation, constrained connectivity, and high-availability requirements in industrial environments.

10. Ability to translate operator and engineering needs into clear user stories, application designs, and supportable software solutions.

11. Demonstrated ownership of production applications, including troubleshooting, incident response, documentation, and continuous improvement.

Preferred Qualifications

1. Master's degree in Computer Science, Software Engineering, Human-Computer Interaction, or a related field.

2. Experience with Microsoft Azure application and AI services, Microsoft Foundry, Azure Functions, Azure App Service, Azure Kubernetes Service, or equivalent platforms.

3. Experience building operator-facing applications for inspection, asset performance, alarm management, field execution, or industrial decision support.

4. Experience developing generative AI, retrieval-augmented generation (RAG), agentic, or multimodal applications with effective grounding and guardrails.

5. Experience integrating computer vision outputs, images, video, annotations, and human-review workflows into production applications.

6. Experience with offline-first or progressive web applications, mobile development, and synchronization for disconnected environments.

7. Experience with real-time user experiences using WebSockets, messaging, notifications, or event-streaming technologies.

8. Experience with design systems, accessibility standards, usability testing, and human-centered design for operational users.

9. Familiarity with historians, industrial IoT platforms, OPC UA, MQTT, digital twins, knowledge graphs, or asset-context models.

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

Critical Selection Criteria

1. Application Engineering Capability: Demonstrated ability to design, build, test, deploy, and sustain secure production applications that integrate AI services and operational data.

2. User and Operational Focus: Ability to translate industrial workflows into intuitive experiences that clearly communicate AI outputs, uncertainty, and required human actions.

3. Industrial Technology, Security, and AI Understanding: Knowledge of industrial operations, application security, constrained connectivity, and the integration patterns required to use AI safely in operational environments.

4. Collaboration and Delivery: Ability to work across product, design, AI, data, architecture, cybersecurity, and operations teams to deliver usable and supportable solutions.