Applied AI Researcher

Applied AI Researcher

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking an Applied AI Researcher to join our team in New Jersey, New Jersey (US-NJ), United States (US).

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.

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.

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, Tensor Flow, 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.

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, Sage Maker, 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.

Initial screening questions:

• What research idea did you convert into a prototype or production capability?
• How would you design an evaluation harness for an LLM-based banking use case such as KYC, underwriting, or sanctions screening?
• How do you determine whether fine-tuning, RAG, prompting, or model adaptation is the right approach?
• How do you account for latency, cost, safety, and AWS/cloud deployment constraints in applied research?
• What failure modes did you discover and how did you mitigate them?
• How do you communicate model limitations to non-research stakeholders?

NTT DATA provides a reasonable range of compensation for U.S.

-based positions. The starting pay range for this role will depend on the nature of the role offered and will either be [$139,872 - $209,808], or [$80-$100] if the role is hired as a temporary position. 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…