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AI​/ML Solution Architect – Data & Analytics

Job in Milwaukee, Milwaukee County, Wisconsin, 53244, USA
Listing for: Johnson Controls
Full Time position
Listed on 2025-12-01
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below

AI/ML Solution Architect – Data & Analytics

Johnson Controls International (JCI) is seeking an AI/ML Solution Architect to join our innovative and impact-driven Data Science and Analytics team. This role is ideal for a seasoned expert with a deep understanding of machine learning, AI, and cloud data platforms, and a strong grasp of the latest advancements in Generative AI and Large Language Models (LLMs).

Base pay range

$/yr - $/yr

Responsibilities

Solution Architecture & System Design

  • Design comprehensive end-to-end AI/ML solution architectures for complex enterprise use cases, spanning data ingestion, feature engineering, model training, deployment, inference, and monitoring.
  • Create and maintain reference architectures and reusable architectural patterns for GenAI applications, including retrieval‑augmented generation (RAG), multi‑agent systems, and multi‑modal AI solutions.
  • Architect scalable solutions that balance technical requirements, business constraints, timelines, and cost considerations.
  • Lead architecture reviews and provide technical guidance on solution design to data science and engineering teams.
  • Establish technical standards, best practices, and governance frameworks for AI/ML solution development across the organization.

Advanced Analytics, LLMs & Modeling

  • Design and implement advanced machine learning models including deep learning, time‑series forecasting, recommendation engines, and LLM‑based solutions (e.g., GPT, LLaMA, Claude).
  • Develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models.
  • Fine‑tune or prompt‑engineer foundation models (e.g., OpenAI, Azure OpenAI, Hugging Face) for domain‑specific applications.
  • Evaluate and optimize LLM performance, latency, cost‑effectiveness, and hallucination mitigation strategies for production use.

Data Strategy & Engineering Collaboration

  • Work closely with data and ML engineering teams to integrate LLM‑powered applications into scalable, secure, and reliable pipelines.
  • Contribute to the development of retrieval‑augmented generation (RAG) architectures using vector databases (e.g., FAISS, Azure Cognitive Search).
  • Support the deployment of models using MLOps principles, ensuring robust monitoring and lifecycle management.

Business Impact & AI Strategy

  • Partner with cross‑functional stakeholders to identify opportunities for applying LLMs and generative AI to solve complex business challenges.
  • Lead workshops or proofs‑of‑concept to demonstrate value of LLM use cases across business units.
  • Translate complex model outputs, including those from LLMs, into clear insights and decision support tools for non‑technical audiences.

Thought Leadership & Mentorship

  • Act as an internal thought leader on AI and LLM innovation, keeping JCI at the forefront of industry advancements.
  • Mentor and upskill data science team members in advanced AI techniques, including transformer models and generative AI frameworks.
  • Contribute to strategic roadmaps for generative AI and model governance within the enterprise.
Qualifications & Experience
  • Education in Data Science, Artificial Intelligence, Computer Science, or related quantitative discipline.
  • 5+ years of hands‑on experience in data science, including at least 1–2 years working with LLMs or generative AI technologies.
  • Demonstrated success in deploying machine learning and NLP solutions at scale.
  • Proven experience with cloud AI platforms—especially Azure OpenAI, Azure ML, Hugging Face, or AWS Bedrock.
Technical Expertise
  • Proficiency in Python and SQL, including libraries like Transformers (Hugging Face), Lang Chain, PyTorch, and Tensor Flow.
  • Experience with prompt engineering, fine‑tuning, and LLM orchestration tools.
  • Familiarity with data storage, retrieval systems, and vector databases.
  • Strong understanding of model evaluation techniques for generative AI, including factuality, relevance, and toxicity metrics.
Leadership & Soft Skills
  • Strategic thinker with a strong ability to align AI initiatives to business goals.
  • Excellent communication and storytelling skills, especially in articulating the value of LLMs and advanced analytics.
  • Strong collaborator with a…
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