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Director of AI & Automation Strategy

Job in Flower Mound, Denton County, Texas, 75027, USA
Listing for: FFF_Enterprises
Full Time position
Listed on 2026-05-28
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer, Data Engineer, Data Science Manager
Salary/Wage Range or Industry Benchmark: 150000 - 200000 USD Yearly USD 150000.00 200000.00 YEAR
Job Description & How to Apply Below
Position: Director of AI & Intelligent Automation Strategy

Position Summary

The Director of AI/ML leads the strategy, architecture, and delivery of enterprise artificial intelligence and robotic process automation (RPA) capabilities across the organization’s data platform. This role is responsible for building and leading a team of AI engineers and automation developers delivering machine learning, generative AI, multi-agent systems, and intelligent automation solutions. The Director partners closely with Data Engineering, Reporting, and business teams to operationalize AI and automation capabilities using Databricks and Microsoft technologies.

This role ensures solutions are scalable, secure, and governed while delivering measurable operational efficiency and business value.

AI Strategy & Architecture Leadership

Lead the development and execution of enterprise AI and intelligent automation strategy.

Activities include:

  • Define and implement the enterprise AI architecture using Databricks AI capabilities.
  • Establish architectural standards for generative AI, machine learning models, and agent‑based systems.
  • Design frameworks for multi‑agent AI systems that support business workflows and decision‑making.
  • Identify high‑impact AI and automation opportunities across business units.
  • Align AI initiatives with the enterprise data platform and overall technology strategy.
AI Engineering & Automation Leadership

Build and lead a high‑performing team responsible for delivering AI and intelligent automation solutions.

Activities include:

  • Lead, mentor, and develop a team of AI engineers, machine learning engineers, and automation developers.
  • Establish engineering standards and best practices for AI development and automation implementation.
  • Oversee the design, development, testing, and deployment of AI and automation solutions.
  • Coordinate work across AI engineering, data engineering, reporting teams, and business stakeholders.
Agentic AI & Generative AI Platforms

Lead the design and implementation of modern AI architectures including generative AI and agentic systems.

Activities include:

  • Implement multi‑agent AI architectures for automation and operational decision support.
  • Deploy AI solutions leveraging Databricks capabilities including MLflow, vector search, and model serving.
  • Implement Retrieval Augmented Generation (RAG) architectures using governed enterprise data.
  • Lead adoption of conversational AI platforms including Databricks Genie and Microsoft Copilot integrations.
Intelligent Automation & RPA Leadership

Lead the robotic process automation (RPA) function within the department and drive enterprise automation initiatives.

Activities include:

  • Define the enterprise strategy for robotic process automation using Microsoft Power Automate.
  • Identify business processes suitable for automation and prioritize initiatives based on operational impact.
  • Oversee the design and development of automation workflows and automated business processes.
  • Integrate AI models and agent‑based systems into automation workflows to enable intelligent decision‑making.
  • Establish governance, monitoring, and reliability standards for automation solutions.
  • Collaborate with business units to streamline processes and reduce manual effort through automation.
AI Governance & Responsible AI

Ensure AI and automation solutions comply with enterprise governance, security, and compliance requirements.

Activities include:

  • Establish policies and standards for responsible AI and intelligent automation.
  • Ensure compliance with enterprise security, privacy, and data governance policies.
  • Implement evaluation frameworks to measure AI performance, accuracy, and reliability.
  • Maintain transparency and auditability of automated and AI‑driven decisions.
Cross‑Functional Collaboration

Partner with technology and business leaders to operationalize AI and automation capabilities.

Activities include:

  • Collaborate with Data Engineering teams to leverage curated enterprise data for AI solutions.
  • Partner with Reporting and Analytics teams to embed AI outputs into business insights and decision tools.
  • Work with business stakeholders to translate operational challenges into AI‑enabled and automated solutions.
  • Communicate AI and automation capabilities, risks, and…
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