Our Cognitive and Generative-AI team is responsible for developing and implementing AI-driven solutions that enhance and scale AI adoption across our firm. This is a pivotal role in our AI Center for Enablement, leading the design and implementation of cutting-edge AI systems. This role leverages and adapts state-of-the-art large language models (LLMs) to solve complex business problems and identifies opportunities to build modular and reusable components. You will collaborate with our Technology Services and Business partners to build and drive solutions. You will provide delivery and ongoing support for data science, advanced analytics, and augmented intelligence technologies, executing on the strategic vision and ensuring the successful implementation of AI initiatives across the firm. Successful candidates will exhibit excellent problem-solving skills, effective communication and analytical skills, as well as strong self drive.
Responsibilities - What You’ll Do
- Provide platform, engineering, and enablement services to drive the adoption and utilization of Generative AI capabilities, fostering technological innovation and improving member experience through foundational AI capabilities.
- Design, develop and deploy AI solutions with a focus on: clarify the problem, design the approach, write the prompts and prototype quickly.
- Curate, process and augment high-quality multimodal data while integrating Retrieval-Augmented Generation (RAG) for robust model grounding and improved accuracy.
- Implement retrieval-augmented workflows: grounding, chunking, indexing strategies, response synthesis, and guardrails for factuality and tone.
- Orchestrate multi-step agents: task decomposition, tool-use design, memory and context strategies, and failure/retry logic for reliability.
- Operationalize prompt systems: reusable prompt patterns, role/task separation, evaluation harnesses, and continuous refinement.
- Leverage core engineering competencies in GPT algorithms, data ingestion for Large Language Models (LLMs), prompt engineering, and Natural Language Processing (NLP)/Chatbot interface construction.
- Deploy solutions to a shareable environment for stakeholders.
- Document decisions: assumptions, risks, test plans, and clear instructions so others can run, test, and extend your prototypes.
- Communication and influence: Translate complex AI behavior into simple narratives and demos. Gather feedback from non-technical stakeholders and close the loop with measurable improvements.