Director, AI & Machine Learning

FFF Enterprises•Flower Mound, TX

About The Position

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. The role builds and leads a team of AI engineers and automation developers delivering machine learning, generative AI, multi-agent systems, and intelligent automation solutions. The Director partners with Data Engineering, Reporting, and business teams to operationalize these capabilities using Databricks and Microsoft technologies, ensuring solutions are scalable, secure, governed, and deliver measurable operational efficiency and business value. The Director develops enterprise AI architecture and standards; leads AI engineering and automation teams; oversees generative AI, agentic AI, and RPA initiatives; and establishes governance, security, evaluation, monitoring, and reliability practices. The role works with technical and business stakeholders to identify opportunities, translate operational challenges into AI-enabled solutions, and communicate capabilities, risks, and value. The position also carries general organizational responsibilities, including following company policies and regulations, meeting assigned goals, collaborating professionally, keeping stakeholders informed, and performing other duties as assigned. The work is performed in a normal office setting. Physical requirements include prolonged computer-screen viewing and sitting, use of wrists, hands, and fingers, occasional lifting of up to 20 pounds, occasional kneeling and bending, and occasional travel. The employee must be able to work the hours and days required to perform the essential functions as scheduled. Mental demands include learning, concentration, working under pressure, attention to detail, teamwork, effective interaction with coworkers and customers, discretion, sound judgment, and proactive problem-solving.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field, or four years of relevant experience in lieu of a degree.
At least 10 years of experience in artificial intelligence, machine learning, automation, or related technical roles, including five years of leadership experience managing technical teams.
Advanced knowledge of artificial intelligence, machine learning, and generative AI technologies.
Knowledge of agent-based AI architectures, multi-agent systems, robotic process automation concepts, and intelligent automation strategies.
Understanding of enterprise AI governance, security, and model lifecycle management.
Strong leadership and team management capabilities.
Expertise designing enterprise AI architectures and machine learning platforms.
Experience implementing generative AI and agent-based systems.
Experience with Databricks AI capabilities, including MLflow and model serving.
Experience integrating AI capabilities with Microsoft Copilot platforms.
Strong strategic thinking and problem-solving abilities; excellent verbal and written communication skills; and ability to translate business challenges into AI and automation solutions.
Ability to meet the position’s physical and scheduling requirements, including prolonged computer use and sitting, occasional lifting up to 20 pounds, occasional kneeling and bending, and occasional travel.

Nice To Haves

Familiarity with Microsoft Copilot platforms and conversational AI technologies.
Familiarity with Databricks AI platform capabilities, including MLflow and vector search.
Knowledge of vector databases, embeddings, and semantic AI architectures.
At least 12–15 years of progressive experience in AI engineering, machine learning engineering, intelligent automation, or enterprise data platform development.
Experience leading AI and automation initiatives using platforms such as Databricks, Microsoft Copilot, and Microsoft Power Automate.
Experience implementing robotic process automation using Microsoft Power Automate.
Experience with conversational AI platforms such as Databricks Genie.
Experience implementing multi-agent orchestration frameworks.
Experience integrating AI and automation systems with enterprise applications such as ERP, CRM, or logistics platforms.
Experience with AI-based video or image processing technologies.

Responsibilities

Lead the development and execution of enterprise AI and intelligent automation strategy.
Define and implement enterprise AI architecture using Databricks AI capabilities and establish standards for generative AI, machine learning models, and agent-based systems.
Design frameworks for multi-agent AI systems supporting business workflows and decision-making, and identify high-impact AI and automation opportunities.
Align AI initiatives with the enterprise data platform and overall technology strategy.
Build, lead, mentor, and develop 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 engineering, reporting, and business teams.
Implement multi-agent AI architectures and deploy solutions using Databricks capabilities, including MLflow, vector search, and model serving.
Implement Retrieval Augmented Generation (RAG) architectures using governed enterprise data, and lead adoption of conversational AI platforms including Databricks Genie and Microsoft Copilot integrations.
Lead the RPA function and enterprise automation initiatives using Microsoft Power Automate; identify and prioritize suitable business processes.
Oversee automation workflows and business processes, integrate AI models and agent-based systems into workflows, and establish governance, monitoring, and reliability standards.
Collaborate with business units to streamline processes and reduce manual effort through automation.
Establish responsible AI and intelligent automation policies and standards, and ensure compliance with enterprise security, privacy, and data governance requirements.
Implement evaluation frameworks for AI performance, accuracy, and reliability, and maintain transparency and auditability of automated and AI-driven decisions.
Partner with Data Engineering teams to use curated enterprise data and with Reporting and Analytics teams to embed AI outputs into business insights and decision tools.
Translate operational challenges into AI-enabled and automated solutions and communicate AI and automation capabilities, risks, and value to technical and non-technical audiences.
Follow company policies, procedures, regulations, and laws; meet assigned goals; represent the company professionally; collaborate constructively; keep stakeholders informed; comply with injury prevention and compliance program policies; and perform other duties as assigned.