Senior AI Engineer
- Compensation
- Not disclosed
- Location
- Cork
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Ai engineering
- Posted
Skills
Nice to have
- 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.
Full description
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.
As this is a Cork-based hybrid role requiring attendance in the office three days per week, candidates who are not currently based in Cork or the surrounding area would be expected to relocate.
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.