Applied AI Researcher

Job Summary

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

• Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, synthetic data, and agentic AI.
• Design experiments to evaluate model performance, robustness, safety, scalability, interpretability, and enterprise usefulness.
• Prototype AI solutions for use cases such as document intelligence, financial analysis, compliance support, knowledge retrieval, and operational automation.
• Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, and business outcome metrics.
• Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques.
• Collaborate with engineers to convert prototypes into production-ready systems with clear requirements, limitations, and acceptance criteria.
• Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.
• Produce internal research papers, technical notes, implementation guides, and thought‑leadership materials.

• 7+ years of experience, with strong research background.
• 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, Tensor Flow, Hugging Face, scikit‑learn, or equivalent research frameworks.
• Ability to design rigorous experiments and communicate findings to technical and business stakeholders.

• Research or applied science experience in banking, finance, compliance, risk, legal, operations, or enterprise knowledge systems.
• Publications, patents, internal research contributions, or open‑source AI contributions.
• Familiarity with Responsible AI, model validation, privacy constraints, and regulated deployment environments.