NTT DATA Services· enterprise· Oct 5, 2026 · 23d ago
Jersey City, NJ, US
Location
Hybrid
Work mode
Full-time
Schedule
Mid
Level
$100K–$160K est.
Estimated compensation
3+ years exp
Experience
Job description
Req ID: 382101
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 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
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.
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.
#LI-NorthAmerica, #DataFID
About NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, us.nttdata.com .
NTT DATA endeavors to make us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at us.nttdata.com . This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here . If you'd like more information on your EEO rights under the law, please click here . For Pay Transparency information, please click here .
Requirements
Must-haves
Req ID: 382101
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 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
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.
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.
Nice-to-haves
Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field.
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.
#LI-NorthAmerica, #DataFID
Application prep
Use the role’s language and application details while tailoring your materials.