Hybrid AI Engineer — Generative AI & ML Pipelines

Johnson Controls; Glendale (CA); Hybrid; USD 85,000 - 110,000; Full time

Benefits offered by this job

Competitive salary

Paid vacation/holidays/sick time

401K and medical/dental/vision

On the job training

Collaborative team environment

Zero Harm Safety

Job summary

Johnson Controls International is seeking an AI Engineer to join our Data Science and Analytics team in Glendale, with a hybrid work arrangement. You will independently own end-to-end AI projects—from data pipelines to deployed applications—and apply prompt engineering, fine-tuning, and MLOps practices.

The role requires 2–5 years of software, data, ML engineering, or data science experience, hands-on with LLMs, and experience with cloud AI platforms.

Qualifications

• 2–5 years of software, data, ML engineering, or data science experience.
• Hands-on with LLMs or generative AI.
• Experience building data/ML pipelines and getting AI/ML solutions to production.
• 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.

Responsibilities

• Develop and deploy Generative AI systems and LLM-powered applications.
• Build and maintain data pipelines for AI systems—ingestion, transformation, and ETL across sources.
• Develop ML pipelines and MLOps workflows—training, evaluation, deployment, and monitoring with CI/CD and Docker.
• Partner with cross-functional stakeholders to translate business challenges into AI solutions.
• Guide junior engineers on technical problems and contribute to design decisions.

Skills

Python

SQL

Collaboration

Communication

Problem Solving

Education

Bachelor's degree in Computer Science or related field

Tools

Palantir AIP

Azure ML

Microsoft Agent Framework

Docker

Snowflake

LangChain

Semantic Kernel

Job description

Johnson Controls International is seeking an AI Engineer to join our Data Science and Analytics team in Glendale, with a hybrid work arrangement. You will independently own end-to-end AI projects—from data pipelines to deployed applications—and apply prompt engineering, fine-tuning, and MLOps practices.

The role requires 2–5 years of software, data, ML engineering, or data science experience, hands-on with LLMs, and experience with cloud AI platforms.