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 SageMaker/Bedrock, or Google Cloud Vertex AI.
Technical Expertise
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.
Soft Skills
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.
Preferred Qualifications
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.
Johnson Controls does not request pregnancy or HIV testing as a condition for hiring, continued employment, or promotion, in accordance with its commitment to labor equality and non-discrimination.
