AI Engineer (Corp Functions & Supply Chain)

NTT DATA ServicesDallas, TX
Onsite

About The Position

Looking for AI Engineer experience in Corporate Functions and Supply Chain designs, builds and deploys intelligent systems to automate logistics, procurement, inventory management, and business workflows. They bridge data science and software engineering to productionize machine learning models, implement Agentic AI, and integrate LLMs into core enterprise operations.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Operations Research, or a related technical field.
  • 7+ years of high proficiency in languages like Python or Java.
  • Experience with frameworks and libraries such as TensorFlow, PyTorch, LangChain, and LlamaIndex.
  • 5+ years of familiarity with modern data and ML platforms (e.g., Databricks, Oracle Cloud SCM, AWS, or Azure).
  • 3 to 5 years of understanding of Procure-to-Pay, Order-to-Cash, and warehouse/inventory logistics.
  • Compliance with Client’s responsible AI principles and Acceptable Use policy
  • Adherence to data residency, privacy (GDPR, HIPAA where applicable), and 21 CFR Part 11 controls where in scope
  • Third-party risk assessment and SOC 2 Type II (or equivalent) certification
  • Disclosure of subcontractors and offshore delivery locations
  • Disclosure of model providers, training data practices, and any use of client data for model improvement (opt-out required)

Responsibilities

  • Build and productionize machine learning algorithms for demand forecasting, inventory optimization, and logistics routing.
  • Design GenAI assistants and agents to automate supplier evaluation, negotiation, contract review, and vendor management.
  • Deploy Agentic AI to enable autonomous decision-making—such as auto-rerouting shipments during a supply disruption or updating your Enterprise Resource Planning (ERP) without human intervention.
  • Create RAG pipelines and integrate AI models into existing enterprise systems (like Oracle Cloud, SAP, or AWS/Azure) so that AI recommendations flow directly into business execution.
  • Establish continuous performance metrics and guardrails to ensure AI models remain accurate, cost-effective, and aligned with enterprise safety standards.
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