We are currently seeking a AI Foundational Model Engineer to join our team in Jersey City, New Jersey (US-NJ), United States (US). AI Foundation Model Engineer LLM / Agentic AI / Full-Stack AI Engineering Role purpose 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. Client-specific emphasis 3 Hands-on AWS AI and cloud engineering is a major asset because AIRP currently runs on AWS. 3 Candidates should be comfortable working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines. 3 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. Primary ownership 3 Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP. 3 End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement. 3 Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns that can be federated across multiple business use cases. Key responsibilities 3 Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems. 3 Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns. 3 Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns. 3 Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures. 3 Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques where appropriate. 3 Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience. 3 Implement model and application observability, including prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health. 3 Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery. 3 Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records. Must-have candidate profile 3 7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning. 3 Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns. 3 Strong Python engineering skills with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks. 3 Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms. 3 Practical exposure to AWS AI/cloud services or comparable cloud-native AI deployment experience, with ability to ramp quickly on AWS-hosted AIRP patterns. 3 Working knowledge of Terraform/IaC, DevOps pipelines, release management, model evaluation, inference optimization, and secure data handling. Preferred experience 3 Banking, risk, compliance, financial crime, operations, or enterprise technology background. 3 Experience with AWS Bedrock, SageMaker, OpenSearch, Kendra, Lambda, EKS/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways. 3 Exposure to cloud-agnostic application patterns, reusable IaC modules, model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments. 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.