AI/ML Solution Architect – Data & Analytics

Description

• Partner with cross-functional stakeholders to identify opportunities for applying LLMs and generative AI to solve complex business challenges.
• Evaluate and optimize LLM performance, latency, cost-effectiveness, and hallucination mitigation strategies for production use.
• Contribute to strategic roadmaps for generative AI and model governance within the enterprise.
• 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.
• Support the deployment of models using MLOps principles, ensuring robust monitoring and lifecycle management.
• 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.
• Establish technical standards, best practices, and governance frameworks for AI/ML solution development across the organization.
• Lead architecture reviews and provide technical guidance on solution design to data science and engineering teams.
• Develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models.
• Design and implement advanced machine learning models including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions (e.g., GPT, LLaMA, Claude).
• 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.
• 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.
• Mentor and upskill data science team members in advanced AI techniques, including transformer models and generative AI frameworks.
• Work closely with data and ML engineering teams to integrate LLM-powered applications into scalable, secure, and reliable pipelines.

Requirements

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

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance