OT Artificial Intelligence ML Operations and Platform Engineer

We are seeking an OT Artificial Intelligence ML Operations and Platform Engineer to design, build and operate the platforms and automated services that move machine learning solutions from development into dependable industrial use. This role establishes the secure, repeatable and observable foundation for model training, validation, release, deployment, inference, monitoring, retraining and retirement across edge, on-premises and approved cloud environments.

This position is part of the OT Artificial Intelligence Team and provides hands-on engineering leadership for the OT AI platform and MLOps ecosystem. The role creates reusable platform capabilities, delivery pipelines, runtime patterns and lifecycle controls that enable data scientists, machine learning engineers and computer vision engineers to deploy and sustain AI solutions without creating isolated or unsupported technology stacks.

Our team is highly technical, creative and cross-functional. The ML Operations and Platform Engineer will work with OT domain experts, AI engineers, data engineers, solution architects, cybersecurity teams, infrastructure teams and operations personnel to deliver a resilient AI engineering environment. Success means accelerating safe model delivery while maintaining cybersecurity, reproducibility, availability, traceability, supportability and clear operational ownership.

HIGH LEVEL JOB DESCRIPTION/SCOPE

This is a dedicated MLOps and platform engineering role supporting OT AI. It covers platform architecture, environment provisioning, model and artifact management, automated testing and release, deployment orchestration, inference infrastructure, observability, security, reliability, cost management, disaster recovery and lifecycle governance. The role supports AI workloads running in centralized and industrial edge environments, including disconnected and intermittently connected operations.

Key Responsibilities

1. Design, build and operate the shared OT AI platform across development, test, validation and production environments.
2. Create reusable CI/CD and continuous training pipelines for model code, data dependencies, containers, configurations and deployment artifacts.
3. Implement model registries, artifact repositories, experiment tracking, environment versioning and approval workflows that provide end-to-end traceability.
4. Automate model validation, security scanning, policy checks, packaging, promotion, deployment, rollback and retirement.
5. Engineer secure model-serving and batch-inference patterns for industrial edge, on-premises and approved cloud environments.
6. Provision and manage platform infrastructure using containers, orchestration, infrastructure as code, configuration management and automated environment controls.
7. Implement observability for platform health, pipeline execution, model performance, inference latency, resource utilization, data and model drift, and operational impact.
8. Define service-level objectives, alerting, incident response, backup, disaster recovery, high-availability and business-continuity practices for OT AI services.
9. Design platform controls for identity, secrets, certificates, network segmentation, least privilege, vulnerability management, software supply-chain security and audit evidence.
10. Support controlled deployment and synchronization for disconnected or intermittently connected field environments, including staged updates and safe rollback.
11. Monitor capacity, performance, accelerator utilization, licensing and platform cost; recommend scaling and optimization actions.
12. Develop platform documentation, reference implementations, templates, runbooks, support procedures and self-service capabilities for OT AI engineering teams.
13. Partner with AI product teams, OT Architecture, OT Cyber, infrastructure and operations teams to onboard use cases and continuously improve platform reliability and developer experience.

Required

1. BS in Computer Science, Software Engineering, Platform Engineering, Information Systems or a related field, or equivalent experience.
2. 5 or more years of experience in platform engineering, DevOps, site reliability engineering or MLOps, including production support responsibilities.
3. Strong programming and automation skills using Python, shell scripting or equivalent languages.
4. Hands-on experience with containers, Kubernetes or equivalent orchestration, source control, CI/CD, artifact repositories and infrastructure as code.
5. Experience implementing model registries, experiment tracking, automated model testing, release controls and production model monitoring.
6. Experience operating highly available platforms with logging, metrics, tracing, alerting, service-level objectives, incident response and root-cause analysis.
7. Experience securing platforms through identity and access management, secrets and certificate management, vulnerability remediation and software supply-chain controls.
8. Experience deploying and supporting workloads across hybrid cloud, on-premises and edge environments.
9. Understanding of machine learning lifecycles, including training, validation, packaging, serving, monitoring, retraining, rollback and retirement.
10. Understanding of OT cybersecurity, network segmentation, least-privilege access and the availability constraints of industrial environments.
11. Demonstrated ownership of production platforms, including troubleshooting, change management, capacity planning, documentation and continuous improvement.

Preferred

1. MS in Computer Science, Software Engineering, Artificial Intelligence or a related field.
2. Experience with Azure Machine Learning, Microsoft Foundry, Azure Arc, Azure Kubernetes Service, Azure Monitor, Microsoft Fabric or equivalent platforms.
3. Experience with MLflow, Kubeflow, GitHub Actions, Azure DevOps, model-serving frameworks or equivalent MLOps technologies.
4. Experience operating GPU, NPU or other accelerator-enabled infrastructure and optimizing utilization for training and inference.
5. Experience with edge orchestration, fleet management, over-the-air updates and workload deployment in disconnected or bandwidth-constrained environments.
6. Experience supporting computer vision, generative AI, agentic AI, time-series analytics or multimodal workloads in production.
7. Experience implementing policy as code, deployment gates, signed artifacts, software bills of materials and provenance controls.
8. Experience with OpenTelemetry, Prometheus, Grafana or equivalent observability technologies.
9. Familiarity with industrial IoT platforms, historians, OPC UA, MQTT and secure data movement across OT zones.
10. Upstream, midstream or downstream domain experience and comfort working directly with asset and operations personnel.

CRITICAL SELECTION CRITERIA

1. MLOps and Platform Capability - Demonstrated ability to build and operate secure, automated and observable platforms that support the complete machine learning lifecycle.
2. Operational Reliability - Ability to engineer for industrial availability, constrained connectivity, controlled change, recovery, supportability and safe rollback across centralized and edge environments.
3. OT, Security and AI Understanding - Knowledge of OT cybersecurity and operations, platform engineering, and the controls required to deploy and sustain AI workloads responsibly.
4. Collaboration and Service Mindset - Ability to translate AI engineering needs into reusable platform capabilities and communicate service health, risk, trade-offs and priorities to technical and nontechnical stakeholders.