AI Foundation Model Engineer

At NTT DATA, we know that with the right people on board, anything is possible. The quality, integrity, and commitment of our employees have been key factors in our company's growth and market presence. By hiring the best people and helping them grow both professionally and personally, we ensure a bright future for NTT DATA and for the people who work here.
For more than 25 years, NTT DATA Services have focused on impacting the core of your business operations with industry-leading outsourcing services and automation. With our industry-specific platforms, we deliver continuous value addition, and innovation that will improve your business outcomes. Outsourcing is not just a method of gaining a one-time cost advantage, but an effective strategy for gaining and maintaining competitive advantages when executed as part of an overall sourcing strategy.
NTT DATA Services currently seeks a AI Foundation Model Engineer to join our team in Jersey City, New Jersey.

Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic workflows. The role converts AI concepts into secure, scalable, observable, and supportable production systems on the enterprise AI-ready platform (AIRP), which is currently AWS-hosted while following a cloud-agnostic architecture blueprint.

• Hands-on AWS AI and cloud engineering is a major asset because AIRP currently runs on AWS.
• Candidates should be comfortable working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines.
• Experience should map to business AI use cases such as KYC, credit underwriting, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.

• Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP.
• End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
• Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns that can be federated across multiple business use cases.

• Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems.
• Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns.
• Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns.
• Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures.
• Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques where appropriate.
• Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience.
• Implement model and application observability, including prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health.
• Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery.
• Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records.

• 7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning.
• Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns.
• Strong Python engineering skills with PyTorch, Tensor Flow, Hugging Face, Lang Chain, Llama Index, Semantic Kernel, or equivalent frameworks.
• Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms.
• Practical exposure to AWS AI/cloud services or comparable…