Applied AI Researcher

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.

Key responsibilities

·   Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, synthetic data, and agentic AI.

·   Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques.

·   Document model limitations, data assumptions, hallucination patterns, bias risks, performance boundaries, and control recommendations for regulated deployment.

·   Collaborate with engineers to convert prototypes into production-ready AIRP requirements, including latency, cost, observability, security, and AWS/cloud deployment considerations.

·   Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.

Must-have candidate profile

·   Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field.

·   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.