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Director, Applied AI
Job in
Austin, Travis County, Texas, 78716, USA
Listed on 2026-06-03
Listing for:
DSV - Global Transport and Logistics
Full Time
position Listed on 2026-06-03
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Position Summary
The Director, Applied AI – North America leads the identification, development, and deployment of artificial intelligence capabilities across DSV Contract Logistics' North America shared business functions and core warehouse and distribution operations. This is a greenfield, builder role: the Director will personally architect and ship AI solutions — agents, fine‑tuned models, and integrated workflows — that drive measurable improvements. The Director will establish the AI capability for North America from the ground up, set the standards and governance practices that protect the business, and represent North America in DSV's global AI governance forums.
EssentialDuties And Responsibilities
- Identify, prioritize, and quantify high‑value AI use cases across Operations, BCM, Engineering, Shared Services, P+O, and other functions; build the multi‑year AI roadmap and align it to enterprise growth, savings, and transformation targets.
- Personally design, build, and ship AI solutions, including LLM‑based agents, fine‑tuned models, retrieval pipelines, and intelligent automations integrated into the Microsoft 365 ecosystem, DSV’s WMS, TMS, and adjacent operational systems.
- Establish and own the North America AI engineering practices, including model selection, evaluation, prompt and agent design standards, MLOps, observability, and cost control.
- Define and operate the AI governance, risk, security, and responsible‑AI framework for North America in alignment with DSV global policy; represent North America in DSV's global AI governance bodies.
- Construct rigorous business cases for each AI investment with clear ROI, baseline measurement, and post‑deployment value tracking; partner with Operations Finance to validate realized savings.
- Translate business problems articulated by functional and operational leaders into well‑scoped AI solutions; act as the primary internal consultant for AI feasibility, build‑vs‑buy, and vendor selection decisions.
- Evaluate, pilot, and operationalize commercial AI capabilities embedded in the existing technology stack, including Microsoft 365 Copilot, Coupa AI, and AI features in WMS, TMS, and ERP platforms; negotiate value where appropriate.
- Build and lead a small, high‑performing team of AI builders over time; recruit, develop, retain, and motivate technical talent capable of operating in a complex matrix environment.
- Establish KPIs and OKRs that track adoption, model performance, and realized business value; report progress to North America executive leadership and contribute to global AI reporting.
- Partner closely with IT, Information Security, Legal, Privacy, P+O, and Finance to ensure deployments meet enterprise standards for data handling, access control, and regulatory compliance.
- Develop and deliver targeted enablement to functional leaders and operators so AI tools are adopted, trusted, and used at scale.
- Operate fluently across executive (C‑Level) and operator (warehouse, procurement, real estate) audiences, modulating depth and language to fit.
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or an equivalent quantitative field is required.
- Minimum 10 years of progressive professional experience, with a minimum of 5 years building and shipping production AI/ML systems and a minimum of 3 years in a senior or lead capacity within a matrixed enterprise.
- Demonstrated track record of personally shipping AI solutions to production and quantifying realized business value.
- Experience standing up an AI capability or function from a greenfield state is strongly preferred.
- Strong, current proficiency in Python and modern AI/ML libraries (e.g., PyTorch, Hugging Face Transformers, scikit‑learn).
- Hands‑on experience with leading LLM ecosystems (OpenAI, Anthropic, Azure AI Foundry); agent frameworks (e.g., Lang Graph, Auto Gen, MCP); retrieval architectures and vector databases.
- Working knowledge of model fine‑tuning, evaluation methodologies, and MLOps tooling.
- Strong fluency in the Microsoft 365 ecosystem, including Copilot, Power Platform, Microsoft Fabric, and Azure AI services.
- Familiarity with…
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