AI Foundation Model Engineer

t 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 abright 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 I Foundation Model Engineer to join our team in Jersey City, New Jersey.

Role purpose
Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic AI workflows. The role converts AI concepts into secure, scalable, observable, and supportable production systems suitable for a regulated financial-services environment.
Primary ownership

• Production LLM applications, RAG pipelines, AI services, and model-serving integrations.
• End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
• Model adaptation, inference optimization, APIs, observability, and operational readiness for GenAI solutions.

• 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.
• dapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques.
• Develop scalable APIs, microservices, model-serving components, and integration patterns across cloud, hybrid, or containerized environments.
• 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, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms.
• Practical knowledge of model evaluation, fine-tuning, inference optimization, and secure data handling.

• Banking, risk, compliance, financial crime, operations, or enterprise technology background.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.
• Exposure to model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments