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About This Role
About Us
Johnson Controls, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, reduce carbon emissions, and operate with the precision and resilience required in rapidly expanding industries such as data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education.
For more than 140 years, Johnson Controls has delivered performance where it really matters. Backed by advanced technology, lifecycle services and an industry-leading field organization, we elevate customer performance, turn goals into real-world results and help move society forward.
Johnson Controls International (JCI) is looking for an AI Platform Engineer to join the AI Engineering & Platforms team. This role focuses on building, automating, securing, and operating the enterprise landing zones and shared services that enable AI, machine learning, generative AI, and agent-based solutions across JCI.
You will work across cloud platform engineering, infrastructure as code, DevOps, networking, security, observability, and service operations. The role is platform-first: creating reusable capabilities, governed deployment patterns, and reliable self-service experiences for AI engineering, data science, product, and business teams.
How you will do it
Enterprise AI Landing Zones & Cloud Platform Engineering
- Build and operate secure, scalable AI landing zones and shared platform services, primarily on Azure and increasingly across AWS.
- Develop reusable infrastructure modules and deployment patterns using Terraform and Terragrunt, with consistent configuration across development, test, and production environments.
- Provision and support Azure AI Foundry, Azure Machine Learning, Azure OpenAI, AI Search, Document Intelligence, Container Apps, App Services, Function Apps, Key Vault, Azure Container Registry, Storage, Cosmos DB, Redis, Application Insights, Log Analytics, and supporting services.
- Contribute to AWS AI/ML platform capabilities using services such as Amazon Bedrock, SageMaker, API Gateway, Lambda, S3, IAM, and associated networking and security services.
- Design platform services so new capabilities can be onboarded through repeatable modules, documented standards, and approved deployment patterns.
AI Gateways, Model Access & Runtime Services
- Build and operate governed access to foundation models and AI services through Azure API Management and equivalent cloud gateway patterns.
- Implement authentication, authorization, secrets management, rate limiting, quotas, routing, retries, circuit breakers, usage tracking, and cost controls for model and API consumption.
- Support pay-as-you-go and provisioned-throughput model deployments, model upgrades, endpoint lifecycle management, capacity planning, and controlled access for consuming teams.
- Enable secure hosting patterns for APIs, agents, containerized workloads, scheduled jobs, and event-driven services using managed cloud platforms.
Automation, CI/CD & Developer Enablement
- Create and maintain Azure DevOps pipelines for infrastructure deployment, validation, policy checks, application hosting, and controlled releases.
- Build automated testing for Terraform modules, platform services, networking, security controls, and deployment smoke tests.
- Create sample projects, templates, reference implementations, runbooks, and onboarding documentation that help teams adopt the platform safely and quickly.
- Work with consuming teams to troubleshoot platform access, networking, identity, certificates, private endpoints, deployment, and runtime issues.
Security, Governance, Reliability & Operations
- Implement private networking, managed identities, Entra ID integration, RBAC, least-privilege access, secrets management, logging, auditability, and secure-by-default configurations.
- Partner with Cybersecurity, Cloud Engineering, Networking, Enterprise Architecture, AI Governance, and service owners to move solutions from development into supported production environments.
- Build dashboards, alerts, health checks, monitoring, and operational metrics for platform availability, model usage, cost, capacity, and service quality.
- Participate in incident response, root-cause analysis, problem management, platform maintenance, change management, and continuous improvement.
- Support platform request, approval, provisioning, documentation, and handover processes, ensuring solutions remain supportable and compliant.
Collaboration & Engineering Standards
- Collaborate with AI engineers, data scientists, solution architects, product teams, and cloud specialists to translate use cases into reusable platform capabilities.
- Contribute to architecture reviews, technical design decisions, backlog refinement, sprint delivery, code reviews, and platform roadmap planning.
- Promote engineering practices focused on automation, reliability, security, maintainability, cost awareness, and clear technical documentation.
Qualifications & Skills
Required
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline, or equivalent practical experience.
- 5+ years of experience in cloud, DevOps, platform, SRE, infrastructure, or MLOps engineering roles.
- Hands-on experience building production Azure solutions using Terraform and Azure DevOps.
- Strong knowledge of cloud networking, private endpoints, DNS, identity, RBAC, managed identities, secrets, and secure service-to-service connectivity.
- Experience with containerized and managed application platforms such as Azure Container Apps, App Services, Functions, Docker, or Kubernetes.
- Experience implementing CI/CD, automated testing, deployment controls, monitoring, alerting, and operational support for production services.
- Proficiency in at least one scripting or programming language such as Python, PowerShell, or Bash.
- Ability to diagnose complex infrastructure, networking, authentication, certificate, deployment, and runtime issues.
- Clear communication and documentation skills, with the ability to work across engineering, architecture, security, governance, and product teams.
Preferred
- Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, Document Intelligence, or similar managed AI services.
- Experience designing or operating API gateways using Azure API Management, including policies for authentication, routing, throttling, retries, observability, and usage controls.
- Experience developing reusable Terraform modules and working with Terragrunt, remote state, module versioning, and environment promotion patterns.
- Experience with enterprise landing zones, hub-and-spoke networking, private cloud services, policy enforcement, and multi-subscription or multi-account environments.
- Experience operating AI or ML platforms, including model endpoints, capacity, quotas, evaluation services, vector search, RAG infrastructure, or agent runtimes.
- Working knowledge of cloud cost management, service reliability, incident management, and platform product thinking.
Nice to have
- Experience with AWS platform engineering, including Bedrock, SageMaker, API Gateway, IAM, Lambda, S3, VPC networking, Terraform, or Terragrunt.
- Experience with multi-cloud AI gateway, model-routing, or centralized model-access patterns.
- Knowledge of LLMOps, agent orchestration, MCP, RAG, embeddings, vector databases, model evaluation, safety, or observability patterns.
- Experience with OpenTelemetry, SIEM integration, dashboards, load testing, resilience testing, or performance engineering.
- Exposure to ServiceNow-based request and fulfilment processes, CMDB, application onboarding, or enterprise architecture approval processes.
- Relevant certifications such as Azure Administrator, Azure Solutions Architect, Azure DevOps Engineer, Azure AI Engineer, AWS Solutions Architect, or Terraform Associate.
- Experience supporting global teams and services across multiple regions, time zones, and regulatory environments.
Mindset
- Platform-first engineer who looks for reusable solutions rather than one-off deployments.
- Security-conscious and comfortable balancing delivery speed with enterprise controls.
- Operationally minded, with a focus on reliability, supportability, automation, and continuous improvement.
- Pragmatic problem solver who can work through ambiguity and evolving cloud and AI technologies.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Johnson Controls, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Johnson Controls AI Hiring
Johnson Controls has 3 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Milwaukee, WI, US, Irving, TX, US, Irving, TX. Compensation range: $217K - $217K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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