Applied AI Researcher

Applied AI Researcher

• Strong foundation in machine learning, deep learning, NLP, transformers, information retrieval, and generative AI.
• Hands-on experience with LLMs, embeddings, RAG, model evaluation, and applied GenAI experimentation.
• Python skills with PyTorch, TensorFlow, Hugging Face, scikit-learn, or equivalent research frameworks.
• Ability to design rigorous experiments and communicate findings to technical, product, business, risk, and governance stakeholders.
• Ability to translate research results into production requirements suitable for an AWS-hosted enterprise platform.

Role purpose

Bridge advanced AI research and practical enterprise use cases by validating models, methods, and prototypes that can become production-grade AIRP solutions. The role focuses on measurable business value, rigorous experimentation, model behavior, and safe translation of research into banking-relevant applications.

Client-specific emphasis

• Research must be grounded in enterprise business use cases, not generic AI experimentation.
• Candidates should understand how model, retrieval, data, evaluation, latency, cost, and safety decisions affect production delivery on AIRP.
• Cloud/AWS awareness is valuable because successful research outputs must be handed off to engineering teams building on AWS-hosted AIRP.

Primary ownership

• Applied research agenda for LLMs, NLP, RAG, evaluation, multimodal AI, and agentic workflows relevant to enterprise use cases.
• Prototypes, experiments, benchmark design, model-selection recommendations, and production-readiness evidence.
• Research-to-production handoff with AI engineering, AIRP platform, product, risk, and governance teams.