Senior AI Engineer

Role overview

Responsibilities

• check_circle Design and implement advanced machine learning models including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions (e.g., GPT, LLaMA, Claude).
• check_circle Develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models.
• check_circle Fine-tune or prompt-engineer foundation models (e.g., OpenAI, Azure OpenAI, Hugging Face) for domain-specific applications.
• check_circle Evaluate and optimize LLM performance, latency, cost-effectiveness, and hallucination mitigation strategies for production use.
• check_circle Work closely with data and ML engineering teams to integrate LLM-powered applications into scalable, secure, and reliable pipelines.
• check_circle Contribute to the development of retrieval-augmented generation (RAG) architectures using vector databases (e.g., FAISS, Azure Cognitive Search).
• check_circle Support the deployment of models using MLOps principles, ensuring robust monitoring and lifecycle management.
• check_circle Partner with cross-functional stakeholders to identify opportunities for applying LLMs and generative AI to solve complex business challenges.
• check_circle Lead workshops or proofs-of-concept to demonstrate value of LLM use cases across business units.
• check_circle Translate complex model outputs, including those from LLMs, into clear insights and decision support tools for non-technical audiences.
• check_circle Act as an internal thought leader on AI and LLM innovation, keeping JCI at the forefront of industry advancements.
• check_circle Mentor and upskill AI engineering team members in advanced AI techniques, including transformer models and generative AI frameworks.
• check_circle Contribute to strategic roadmaps for generative AI and model governance within the enterprise.

Basic qualifications

• 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.

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.