Johnson Controls International (JCI) is seeking a Senior AI Engineer- Data Analytics 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 a Senior AI Engineer, 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 AI engineering strategy, mentoring teams, and driving the use of AI to deliver measurable business value.

How you will do it

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 AI engineering 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.

What we look for

• Education in Data Science, Artificial Intelligence, Computer Science, or related quantitative discipline.

• 5+ years of hands-on experience in AI engineering, data science, or machine learning, 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), Microsoft Agent Framework, LangChain, PyTorch, and TensorFlow.

• 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 track record of influencing stakeholders across product, engineering, and executive teams.

Preferred Qualifications

• Experience with IoT, edge analytics, or smart building systems.

• Familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks.

• Knowledge of data privacy and governance considerations specific to LLM usage in enterprise environments.

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