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).

As an AI/ML Solution Architect, you will lead the development and deployment of scalable AI solutions—including those powered by LLMs—to accelerate digital transformation across our products, operations, and customer experiences. You’ll play a critical role in shaping JCI’s data science strategy, mentoring teams, and driving the use of AI to deliver measurable business value.

How you will do it 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…