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AI Engineer

Johnson Controls · San Jose

Nuevo
Mid 🇬🇧 English
Python SQL Azure OpenAI Azure ML AWS SageMaker Docker CI/CD MLOps LangChain Semantic Kernel Microsoft Agent Framework Palantir AIP Snowflake pandas scikit-learn

Descripcion del puesto

About the role

Johnson Controls International is looking for an AI Engineer to join its Data Science and Analytics team. You will own end‑to‑end delivery of AI projects, building pipelines, tooling, and applications that turn LLM models into reliable production software.

Key responsibilities

  • Develop and deploy Generative AI and LLM‑powered applications such as enterprise search, document summarisation, and conversational AI.
  • Design and maintain data pipelines (ETL, ingestion, transformation) across structured and unstructured sources, using Snowflake and Azure.
  • Build and operate ML pipelines and MLOps workflows with CI/CD, Docker, and model serving.
  • Implement retrieval and embedding workflows (RAG, vector databases) for scalable knowledge retrieval.
  • Partner with cross‑functional stakeholders, translate business challenges into AI solutions, and communicate results to non‑technical audiences.
  • Mentor junior engineers and contribute to design discussions and technical decisions.

Required profile

  • 2–5 years of experience in software, data, ML engineering or data science with hands‑on work on LLMs or generative AI.
  • Education in Computer Science, Software Engineering, Data Engineering, Data Science or a related quantitative discipline.
  • Proven success delivering data or ML pipelines and AI/ML solutions to production.

Required skills

  • Python and SQL with strong software‑engineering practices.
  • Experience with Generative AI stack: prompt engineering, fine‑tuning (e.g., LoRA), LLM orchestration, and agent frameworks such as LangChain, Semantic Kernel, Microsoft Agent Framework.
  • Cloud AI platforms: Azure OpenAI/Azure ML, AWS SageMaker/Bedrock, Google Cloud Vertex AI.
  • MLOps tools: CI/CD, Docker, model serving, containerisation.
  • Data engineering tools: Snowflake, ETL pipelines, RAG, vector databases.
  • Data‑science libraries: pandas, scikit‑learn, model evaluation and experimentation.

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Publicado hace 1 hora

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Johnson Controls

San Jose