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.
What we look 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.
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.
#LI-Hybrid
#GOSIA
