Ai Engineer

Johnson Controls•Glendale, AZ

13d•$85,000 - $110,000•Hybrid

About The Position

Johnson Controls International (JCI) is seeking an AI Engineer to join their innovative and impact-driven Data Science and Analytics team. This role is ideal for an engineer who combines solid software, data, and ML engineering skills with hands-on Generative AI experience—and a data scientist's curiosity for how models behave. The AI Engineer will build the pipelines, tooling, and applications that turn AI and LLM models into dependable production software. As an AI Engineer, you will independently own the end-to-end delivery of defined AI projects—from data pipeline through deployed application. You will make sound technical decisions within your scope, partner directly with cross-functional stakeholders, and guide junior engineers on specific problems as you deliver measurable business value.

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—exploratory analysis, statistical modeling, or classic ML (classification, regression, 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 good software engineering habits—testing, version control, and clean code.
Hands-on experience with the Generative AI stack: prompt engineering, fine-tuning (e.g., LoRA), LLM orchestration, and agent frameworks (LangChain, Semantic Kernel, Microsoft Agent Framework).
Experience building ETL and ML pipelines and applying MLOps practices (CI/CD, Docker, model serving).
Familiarity with data science libraries and workflows—pandas, scikit-learn, and 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.
Comfortable presenting technical and analytical work to both technical and non-technical stakeholders.
Self-directed problem solver who manages 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—statistical modeling, experimentation, or deep learning (forecasting, computer vision, or NLP).
Experience with the Microsoft ecosystem (Microsoft 365 Copilot, SharePoint, Power Platform, Snowflake).
Knowledge of data privacy and governance considerations for enterprise LLM usage.

Responsibilities

Develop and deploy Generative AI systems and LLM-powered applications (e.g., GPT, Claude, LLaMA) for use cases 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—that orchestrate tools, retrieval, and reasoning.
Evaluate and improve model outputs for accuracy, relevance, latency, and cost, applying data science techniques to measure and validate performance.
Build and maintain the data pipelines that feed AI systems—ingestion, transformation, and ETL across structured and unstructured sources (e.g., Snowflake, Azure).
Develop and operate ML pipelines and MLOps workflows—training, evaluation, deployment, and monitoring—using CI/CD, containerization (Docker), and model serving.
Build reusable components, services, and APIs around AI models that help the team ship features faster.
Implement retrieval and embedding workflows (RAG, vector databases) for scalable, accurate knowledge retrieval.
Apply software engineering best practices—testing, version control, and code review—across your 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.
Translate model outputs, data findings, and technical tradeoffs into clear insights for 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 raise the bar on engineering and data science practices.