AI/ML Platform Engineer

Overview

Lead design, deployment, and scaling of LLM-driven AI solutions for business impact.

Key Responsibilities

• vector db
• llm optimization
• llm integration
• conversational ai
• model design
• mlops deployment

Tasks

-Lead workshops or proofs-of-concept to demonstrate value of LLM use cases across business units. -Contribute to the development of retrieval-augmented generation (RAG) architectures using vector databases (e.g., FAISS, Azure Cognitive Search). -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. -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. -Develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models. -Contribute to strategic roadmaps for generative AI and model governance within the enterprise. -Design and implement advanced machine learning models including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions (e.g., GPT, LLaMA, Claude). -Partner with cross-functional stakeholders to identify opportunities for applying LLMs and generative AI to solve complex business challenges. -Translate complex model outputs, including those from LLMs, into clear insights and decision support tools for non-technical audiences. -Support the deployment of models using MLOps principles, ensuring robust monitoring and lifecycle management.

Requirements

• azure openai
• hugging face
• python
• langchain
• pytorch
• data science

What You Bring

-Strong collaborator with a track record of influencing stakeholders across product, engineering, and executive teams. -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), LangChain, PyTorch, and TensorFlow. -Excellent communication and storytelling skills, especially in articulating the value of LLMs and advanced analytics. -Fine-tune or prompt-engineer foundation models (e.g., OpenAI, Azure OpenAI, Hugging Face) for domain-specific applications. -Strong understanding of model evaluation techniques for generative AI, including factuality, relevance, and toxicity metrics. -5+ years of hands-on experience in data science, including at least 1–2 years working with LLMs or generative AI technologies. -Experience with prompt engineering, fine-tuning, and LLM orchestration tools. -Strategic thinker with a strong ability to align AI initiatives to business goals. -Familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks. -Knowledge of data privacy and governance considerations specific to LLM usage in enterprise environments. -Education in Data Science, Artificial Intelligence, Computer Science, or related quantitative discipline. -Demonstrated success in deploying machine learning and NLP solutions at scale. -Experience with IoT, edge analytics, or smart building systems. -Familiarity with data storage, retrieval systems, and vector databases.

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About Johnson Controls

-Pioneered the electric thermostat, launching the building controls industry. -Evolved into a global leader in smart building systems through over a century of innovation. -Typical projects range from HVAC installations to integrated smart systems in hospitals, airports, stadiums, and data centers using their OpenBlue digital platform. -Expertise covers HVAC, fire detection and suppression, security systems, energy management, and facility services. -Earned LEED Platinum certification for its North American headquarters and supplied smart systems to landmarks like Burj Khalifa and Taipei 101.

Sector Specialisms