Note: The job is a remote job and is open to candidates in USA. Johnson Controls is a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization. The Senior DevOps Engineer will maintain the platform supporting cloud services, plant systems, and enterprise applications while building AI infrastructure, CI/CD pipelines, infrastructure as code, observability, security, and cost controls. The role also owns reliability practices, incident management, hybrid deployments, and engineering enablement.
Responsibilities
- Azure DevOps as a platform - organization and project structure, YAML pipelines, repositories and branch policies, artifact feeds, service connections, environments, and self-hosted agent pools including agents inside plant and DMZ networks
- Build deployment paths for the systems we actually run. Microservices are the easy case; you will also need controlled, repeatable release processes for integration artifacts, API layers, and configuration for COTS platforms ([CPQ], [PLM], [ERP], [MES]) that do not deploy like modern applications
- Design approval gates, change records, and audit evidence appropriate to systems that control what gets manufactured and shipped, without turning every deployment into a two-week ceremony
- Plan and execute deployments around production reality - maintenance windows, shift schedules, quarter-end order volume, and the fact that the factory does not stop for a release
- Manage infrastructure as code across all environments. Manual portal changes are a defect, not a workflow
- Manage the estate: Functions, Azure SQL and storage, Service Bus and Event Hubs, API Management, and the data platform
- Networking and identity - VNets, private endpoints, hub-and-spoke topology, [ExpressRoute / site-to-site VPN] connectivity to plants, Entra ID, managed identities, Key Vault, and RBAC
- Manage hybrid and edge footprint, including [Azure Arc] for on-premise servers and [IoT Edge / IoT Hub] where shop floor systems require local resilience and cannot depend on WAN availability
- Own environment strategy and lifecycle: dev, test, UAT, and production; environment refresh and provisioning; and test data management including masking of customer, pricing, and design data
- Stand up and operate [Azure Machine Learning / Azure AI Foundry] - workspaces, compute and GPU capacity, model registry, and managed inference endpoints
- Manage Azure OpenAI and model provider access: provisioning, quota and throughput planning, private networking, content filtering configuration, and key and identity management
- Extend CI/CD to AI workloads - versioning for models, prompts, and datasets; automated evaluation as a pipeline gate so a regression blocks a release the same way a failing unit test does; and reliable rollback
- Build observability for AI in production: latency, error and abstention rates, token consumption, drift signals, and per-feature cost attribution
- Deploy inference where it needs to run, including on-premise or edge at plants where latency, connectivity, or data policy rules out a cloud round trip
- Own AI cost management. Make spend attributable and forecastable, and raise the trade-offs early rather than at invoice time
- Build monitoring and alerting that reflects business impact rather than resource metrics - a stalled BOM release matters more than CPU utilization. Own SLOs, dashboards, and incident response for production systems
- Drive incident management and blameless postmortems, and convert findings into platform changes
- Own security posture in the pipeline and the estate: secrets management, vulnerability and dependency scanning, container image hygiene, SBOM generation, and patch management coordination - including OT environments where patching windows are constrained
- Partner with IT, OT, and information security on segmentation, access control, and compliance requirements for plant-connected systems
- Reduce friction and toil deliberately. Self-service environments, golden pipeline templates, and good documentation are deliverables, not side effects
- Mentor engineers on deployment, observability, and operational ownership of what they build
Skills
- 5+ years in DevOps, platform, SRE, or cloud infrastructure engineering
- Hands-on Azure experience across compute, networking, identity, data services, and monitoring
- Azure DevOps expertise - YAML pipelines, self-hosted agents, environments and approvals, artifact management, and repository governance
- Equivalent depth in GitHub Actions or GitLab CI with demonstrated ability to pick up Azure DevOps is acceptable
- Containerization and orchestration in production (Docker and Kubernetes)
- Scripting and automation in Python and PowerShell or Bash
- Experience deploying into hybrid environments, not cloud-only. Firewalled targets, private connectivity, and on-premise servers should be familiar territory
- Demonstrated ownership of production systems - on-call, incident response, and measurable reliability improvement
- MLOps experience: Azure Machine Learning, model registries and inference endpoints, GPU capacity management, or LLMOps including prompt and evaluation versioning and token cost management
- Experience supporting enterprise business systems - ERP, PLM, CPQ, MES, or integration middleware - and realistic expectations about what CI/CD looks like for commercial platforms
- Manufacturing or industrial environment experience, including OT network segmentation and plant cybersecurity practices
- Data platform operations: [Data Factory / Fabric], warehouse or lakehouse infrastructure, orchestration and pipeline reliability
- FinOps practice - cloud cost visibility, allocation, and optimization
- Azure certifications (AZ-400, AZ-104/305, or AI-102) and Kubernetes certifications (CKA)
- Experience being an early platform hire and establishing standards rather than inheriting them
- Production infrastructure-as-code experience with [Terraform / Bicep], including managing multiple environments and state responsibly
- Candidates should be located within commuting distance of the facility or be open to relocating, with relocation assistance available
- For the right candidate, we are also open to considering a fully remote work arrangement
Benefits
- Relocation assistance available
- For the right candidate, we are also open to considering a fully remote work arrangement.
- This role offers a competitive Bonus plan that will take into account individual, group, and corporate performance.
- This position includes a competitive benefits package.
Company Overview
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