Ai Engineer

AI Engineer

Build your best future with the Johnson Controls team. 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 seeking an AI Engineer to join our 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. You 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.

How you will do it

Generative AI Systems & Applications

• 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.

Data, ML & Software Engineering

• 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.

Business Impact & Stakeholder Communication

• 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.

Mentorship & Collaboration

• 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.

Qualifications & Experience

• 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 Sage Maker/Bedrock, or Google Cloud Vertex AI.

Technical Expertise

• Strong proficiency in Python and…