Req ID: 382102

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

Applied AI Researcher

GenAI / NLP / Agentic AI / Applied Machine Learning

Level

Research-focused Individual Contributor

Target / alternate titles

Applied Scientist; Research Scientist - NLP; AI Research Scientist; ML Researcher; NLP Scientist; GenAI Researcher; Applied ML Scientist

Core keywords

applied research, LLM, NLP, transformers, RAG, retrieval, model evaluation, experiments, robustness, embeddings, synthetic data, multimodal, PyTorch, Hugging Face, banking AI, AWS AI, AIRP

Recruiter red flags

Academic-only profile with no applied delivery; weak experimental design; cannot translate research into AIRP-ready business or engineering requirements; no awareness of regulated data constraints.

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, 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.

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.

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 for incentive compensation based on individual and/or company performance. If the position offered in temporary, the position will not be eligible for incentive compensation.

This position is eligible for company benefits that will depend on the nature of the role offered. Company benefits may include medical, dental, and vision insurance, 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.