Applied AI Researcher

Key responsibilities: 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, enterprise usefulness, and production feasibility. Prototype AI solutions for KYC, credit underwriting, governance tracking, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening. Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, business outcome metrics, and risk-specific acceptance criteria. 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.

Basic Qualifications: Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field. 3+ years of experience in machine learning, deep learning, NLP, transformers, information retrieval, and generative AI. 3+ years of experience with LLMs, embeddings, RAG, model evaluation, and applied GenAI experimentation. 3+ years of experience in Python with PyTorch, TensorFlow, Hugging Face, scikit-learn, or equivalent research frameworks. 3+ years of experience designing rigorous experiments and communicating findings to technical, product, business, risk, and governance stakeholders. 3+ years of experience translating research results into production requirements suitable for an AWS-hosted enterprise platform.

Preferred experience: Research or applied science experience in banking, finance, compliance, risk, legal, operations, financial crime, sanctions, or enterprise knowledge systems. Experience with AWS Bedrock, SageMaker, vector search, MLflow, Databricks, model evaluation tooling, or cloud-based experimentation environments. Publications, patents, internal research contributions, open-source AI contributions, or prior research-to-production handoffs. Familiarity with Responsible AI, model validation, privacy constraints, audit documentation, and regulated deployment environments. NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this role is $104,904 - $182,125 per year. Actual compensation will depend on a number of factors, including the candidate's relevant experience, technical skills, and other qualifications. This position may also be eligible for incentive compensation based on individual and/or company performance. This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and AD&D insurance, short and long term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally-required benefits.