Note: The job is a remote job and is open to candidates in USA. Johnson Controls is a global leader in thermal management, building systems, energy efficiency, and decarbonization. The Senior AI Engineer will set the technical direction for AI across sales, engineering, and manufacturing systems, build production AI/ML applications, establish safe deployment practices, improve enterprise data foundations, and partner on delivery.
Responsibilities
- Own the AI/ML architecture and technical strategy for sales, engineering, and manufacturing systems - model selection, data architecture, evaluation approach, deployment topology, and build-versus-buy recommendations
- Partner with product management, sales operations, engineering leadership, and plant operations to translate business problems into a prioritized portfolio of AI work with honest, expected value and honest feasibility
- Establish the engineering standards this domain requires: reproducible training and evaluation, model and prompt versioning, monitoring and drift detection, rollback paths, and audit trails sufficient for a system that influences what gets built and shipped
- Serve as the senior technical voice in design reviews and as a mentor to software and data engineers adopting these techniques. Raise the team's ceiling, not just your own output
- Specification and document intelligence. Extract structured requirements from customer specifications, mechanical schedules, and submittal documents - PDFs, drawings, spreadsheets - and map them to candidate configurations and selections
- Configuration and quoting assistance. Guided selling and configuration recommendation, competitive crossover matching, pricing and discount guidance, and cost estimation for the non-standard and special scope that drives disproportionate engineering hours and margin risk
- Engineering automation. Similar-order retrieval and reuse, BOM anomaly detection that flags a released structure deviating from statistically comparable units, configuration rule mining and conflict detection over historical orders, and engineering change impact analysis
- Manufacturing intelligence. Labor hour and routing time prediction from configuration features, material shortage and schedule risk forecasting, anomaly detection over final run and leak test data, and quality defect prediction. Where it earns its place, computer vision for assembly verification, nameplate and label validation, or completeness checks at station
- Assistive interfaces. Retrieval-grounded assistants over product, application engineering, submittal, and service knowledge for sales engineers, application engineers, and service technicians - including agents that call configurator, PLM, and ERP APIs to do real work rather than just answer questions
- Design for the failure mode that matters here: a plausible-looking wrong answer that reaches production. AI advises; systems of record decide. Human-in-the-loop review, confidence thresholds, graceful abstention, and full traceability are default requirements, not enhancements
- Build evaluation before building features. Define ground truth with the engineers and estimators who own the judgment today, and measure against it continuously in production
- Solve for real deployment constraints - on-premise and edge inference at plants, latency budgets at a build station, intermittent connectivity, OT/IT network segmentation, and plant cybersecurity requirements
- Establish data governance for AI: what customer, pricing, and design data may leave our environment, what may be sent to third-party model providers, retention and tenancy, and how vendor terms are evaluated
- Assess and improve the usability of source data across [CPQ], [PLM], [ERP], and [MES]. Be the person who says plainly when a model cannot succeed until master data, part number governance, or labor reporting accuracy improves - and who then helps drive that fix
- Build the pipelines, feature engineering, and datasets that make repeated AI development cheap rather than one-off
- Work in close partnership with the Program Manager for these systems. They own scope, schedule, and stakeholder accountability; you own technical direction and feasibility. Neither role works without candid, early information from the other about what is actually achievable and when
Skills
- Bachelor's degree in Computer Science, Engineering, Applied Mathematics, Statistics, or a related field
- 5+ years in software or data engineering, with 5+ years building machine learning or AI systems that reached production and real users
- Demonstrated ownership of an AI/ML system in production over time - not proofs of concept. You should be able to describe what broke after launch, how you found out, and what you changed
- Strong Python and SQL. Depth in the modern applied stack: classical and tabular ML (gradient boosting, scikit-learn), deep learning frameworks, and LLM application patterns including retrieval, structured extraction, tool use, and evaluation
- Real fluency with messy enterprise data - ERP and PLM schemas, MES and historian data, semi-structured documents - and the practical experience to know that this is where most of the work lives
- Experience deploying and operating models in production: containerization, CI/CD, cloud ML platforms, monitoring, and cost management
- Experience in manufacturing, industrial, or engineer-to-order environments - HVAC, capital equipment, building products, or similar highly configured products
- Familiarity with the configure-to-order data landscape: BOM structures, variant and option modeling, routings and standard times, engineering change processes
- Document AI and information extraction experience with engineering drawings, specifications, or technical documentation
- Optimization and operations research exposure - scheduling, sequencing, nesting, capacity planning - and the judgment to reach for it instead of ML when it is the right tool
- Computer vision experience in an industrial setting, including the practical realities of lighting, fixturing, and line-side integration
- Edge or on-premise inference experience in an OT environment
- Experience being the first or one of the first senior AI hires somewhere, building the function rather than inheriting it
- 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.
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