AI Engineer
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What you'll do
- 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.
- 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.
- 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.
- 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.
What they're looking 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.
- 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.
- 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.
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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Johnson Controls International (JCI) is seeking a Senior AI Engineer 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.
Qualifications & Experience
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.
Johnson Controls does not request pregnancy or HIV testing as a condition for hiring, continued employment, or promotion, in accordance with its commitment to labor equality and non-discrimination.
Company
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