Ai Engineer

Johnson Controls • Glendale, AZ

17d • $85,000 - $110,000 • Hybrid

About The Position

Johnson Controls International is seeking an AI Engineer to join its Data Science and Analytics team. The role combines software, data, and ML engineering with hands-on Generative AI experience to build pipelines, tooling, and applications that turn AI and LLM models into dependable production software. The AI Engineer independently owns defined AI projects end to end, from data pipeline through deployed application, makes technical decisions within their scope, partners with cross-functional stakeholders, and guides junior engineers on specific problems.

The position develops and deploys Generative AI systems and LLM-powered applications, adapts foundation models for domain-specific uses, builds agentic workflows, and evaluates model outputs for quality, latency, and cost. It also builds data and ML pipelines, reusable services and APIs, and retrieval and embedding workflows; applies MLOps and software engineering practices; and supports business stakeholders through workshops, proofs of concept, and clear communication of technical findings and tradeoffs.

The role is based in Glendale, Wisconsin, with a hybrid work arrangement. Johnson Controls will not sponsor work visas or provide immigration-related employment sponsorship for this position. The hiring salary range is $85,000–$110,000, with the final salary depending on the applicant’s education, experience, knowledge, skills, abilities, internal equity, location, and market data.

Requirements

• Education in Computer Science, Software Engineering, Data Engineering, Data Science, or a related technical or quantitative discipline.
• 2–5 years of experience in software, data, ML engineering, or data science, including hands-on work with LLMs or Generative AI.
• Demonstrated success delivering data or ML pipelines and AI/ML solutions to production.
• Experience with data science fundamentals, such as exploratory analysis, statistical modeling, or classic machine learning (classification, regression, or forecasting).
• Experience with cloud AI platforms such as Azure OpenAI/Azure ML, AWS SageMaker/Bedrock, or Google Cloud Vertex AI.
• Strong proficiency in Python and SQL, with software engineering habits including testing, version control, and clean code.
• Hands-on experience with Generative AI techniques and tools, including prompt engineering, fine-tuning (such as LoRA), LLM orchestration, and agent frameworks.
• Experience building ETL and ML pipelines and applying MLOps practices, including CI/CD, Docker, and model serving.
• Familiarity with data science libraries and workflows such as pandas, scikit-learn, model evaluation, and experimentation.
• Experience with JCI’s stack or comparable platforms, including Palantir AIP, Azure ML, Microsoft Agent Framework, Power Automate, and Snowflake.
• Working knowledge of embeddings, vector databases, and retrieval systems.
• Ability to own projects and communicate progress, risks, and tradeoffs clearly.
• Strong collaboration skills across product, engineering, and business teams.
• Comfort presenting technical and analytical work to technical and non-technical stakeholders.
• Self-directed problem-solving skills and ability to manage priorities independently.

Nice To Haves

• Experience with IoT, edge analytics, or smart building systems.
• Familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks.
• Data science depth in statistical modeling, experimentation, or deep learning, including forecasting, computer vision, or NLP.
• Experience with the Microsoft ecosystem, including Microsoft 365 Copilot, SharePoint, Power Platform, or Snowflake.
• Knowledge of data privacy and governance considerations for enterprise LLM usage.

Responsibilities

• Develop and deploy Generative AI systems and LLM-powered applications for uses such as enterprise search, document summarization, and conversational AI.
• Apply prompt engineering, fine-tuning, and orchestration techniques to adapt foundation models for domain-specific applications.
• Build agentic workflows and task-specific AI agents using Palantir AIP or the Microsoft Agent Framework to orchestrate tools, retrieval, and reasoning.
• Evaluate and improve model outputs for accuracy, relevance, latency, and cost using data science techniques.
• Build and maintain ingestion, transformation, and ETL data pipelines across structured and unstructured sources.
• Develop and operate ML pipelines and MLOps workflows for training, evaluation, deployment, and monitoring using CI/CD, Docker, and model serving.
• Build reusable components, services, and APIs around AI models.
• Implement retrieval and embedding workflows, including RAG and vector databases, for scalable knowledge retrieval.
• Apply testing, version control, and code review practices across projects.
• Partner with cross-functional stakeholders to translate business challenges into AI solutions.
• Support workshops and proofs of concept that demonstrate the value of LLM and agent use cases across business units.
• Communicate model outputs, data findings, and technical tradeoffs clearly to non-technical audiences.
• Guide junior engineers on specific technical problems and code quality.
• Contribute to design discussions and technical decisions within the team.
• Share knowledge and help improve engineering and data science practices.

Benefits

• Competitive salary.
• Paid vacation, holidays, and sick time.
• Comprehensive benefits package including 401(k), medical, dental, and vision care.
• On-the-job and cross-training opportunities.
• Encouraging and collaborative team environment.
• Dedication to safety through the Zero Harm policy.
• Competitive benefits package.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed