Director of AI/ML

Director of AI/ML

Work Arrangement: Hybrid – Onsite in Flower Mound, TX
Compensation: $220,000 to $220,000 PA
Employment Type: Full-Time
Industry: Enterprise Technology / Healthcare & Distribution

Position Overview

We are seeking an experienced and strategic Director of AI/ML to lead the organization's enterprise Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, and Robotic Process Automation (RPA) initiatives.

The Director of AI/ML will be responsible for defining the enterprise AI strategy, architecture, engineering standards, and delivery roadmap while building and leading a high-performing team of AI engineers, machine learning engineers, and automation developers.

This position will work closely with Data Engineering, Reporting & Analytics, IT, and business leadership to identify high-value opportunities and operationalize AI and intelligent automation capabilities across the organization.

The ideal candidate will have deep expertise in enterprise AI architecture, Generative AI, LLMs, Agentic AI, multi-agent systems, RAG, machine learning, Databricks, MLflow, Microsoft Copilot, and intelligent automation. Experience with Microsoft Power Automate/RPA and enterprise application integration is highly desirable.

The Director will ensure AI solutions are scalable, secure, governed, reliable, and aligned with business objectives, while delivering measurable improvements in productivity, operational efficiency, decision-making, and business value.

Key Responsibilities

1. AI Strategy & Architecture Leadership

• Lead the development and execution of the organization's enterprise AI and intelligent automation strategy.
• Define the enterprise AI architecture and technology roadmap.
• Establish architectural standards and best practices for:
• Artificial Intelligence
• Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Agentic AI
• Multi-agent systems
• Retrieval-Augmented Generation (RAG)
• Intelligent automation
• Design scalable frameworks for multi-agent AI systems supporting business workflows and decision-making.
• Identify high-impact AI and automation opportunities across business functions.
• Prioritize AI initiatives based on business value, feasibility, risk, and operational impact.
• Align AI initiatives with the organization's enterprise data platform and overall technology strategy.
• Establish reusable AI architecture patterns, frameworks, and engineering standards.
• Evaluate emerging AI technologies and determine their applicability to enterprise use cases.

2. AI Engineering & Automation Leadership

• Build, lead, mentor, and develop a high-performing team of:
• AI Engineers
• Machine Learning Engineers
• Data/AI Engineers
• Automation Developers
• AI/ML Architects
• Establish engineering standards and development practices for AI and automation solutions.
• Oversee the complete AI/ML development lifecycle, including:
• Architecture
• Design
• Development
• Testing
• Deployment
• Monitoring
• Optimization
• Maintenance
• Coordinate delivery across AI engineering, data engineering, reporting, analytics, IT, and business teams.
• Establish engineering processes for production-grade AI systems.
• Promote automation, reusable components, CI/CD, MLOps, LLMOps, and responsible AI practices.
• Provide technical direction and architectural guidance for complex AI initiatives.

3. Generative AI & Agentic AI

• Lead the design and implementation of enterprise Generative AI platforms and applications.
• Develop enterprise strategies for LLM adoption and deployment.
• Design and implement Agentic AI and multi-agent architectures.
• Establish frameworks for AI agents that can:
• Reason over enterprise data
• Execute business workflows
• Interact with enterprise applications
• Use tools and APIs
• Make contextual decisions
• Collaborate with other AI agents
• Evaluate and implement modern agent orchestration frameworks.
• Establish patterns for human-in-the-loop AI workflows.
• Develop secure and governed agent execution environments.
• Identify opportunities to incorporate AI agents into operational workflows and decision-support processes.

4. RAG, Vector Search & Enterprise Knowledge

• Lead the development of enterprise Retrieval-Augmented Generation (RAG) architectures.
• Design secure architectures for combining LLMs with governed enterprise data.
• Implement enterprise knowledge retrieval and semantic search capabilities.
• Establish architectures utilizing:
• Embeddings
• Vector databases
• Vector search
• Semantic search
• Knowledge graphs
• Hybrid search
• Ensure enterprise data used by AI systems is governed, secure, accurate, and traceable.
• Establish standards for grounding LLM responses in trusted enterprise information.
• Implement evaluation mechanisms to measure RAG quality, relevance, accuracy, and reliability.

5. Databricks AI Platform

• Lead adoption and implementation of Databricks AI capabilities across the enterprise.
• Architect AI/ML solutions leveraging the Databricks platform.
• Utilize capabilities including:
• Databricks AI
• Mosaic AI
• MLflow
• Model Serving
• Vector Search
• Model lifecycle management
• Establish enterprise standards for machine learning model development and deployment.
• Partner with Data Engineering teams to leverage governed lakehouse data for AI solutions.
• Implement scalable AI/ML workflows integrated with the organization's enterprise data platform.
• Establish model monitoring, evaluation, governance, and lifecycle management practices.
• Evaluate and support conversational AI capabilities such as Databricks Genie.

6. Microsoft Copilot & Conversational AI

• Lead adoption and integration of Microsoft AI and Copilot technologies.
• Develop enterprise use cases leveraging:
• Microsoft Copilot
• Microsoft 365 Copilot
• Copilot Studio
• Azure AI capabilities
• Integrate Copilot capabilities with enterprise data, applications, and workflows.
• Develop conversational AI solutions supporting employees, customers, and business operations.
• Establish governance and security standards for enterprise Copilot deployments.
• Identify opportunities to integrate AI assistants and copilots into existing business processes.

7. Intelligent Automation & RPA

• Lead the enterprise Robotic Process Automation (RPA) function.
• Define the organization's intelligent automation strategy.
• Utilize Microsoft Power Automate to design and implement automated workflows.
• Identify manual, repetitive, and high-volume business processes suitable for automation.
• Prioritize automation initiatives based on:
• Operational impact
• Cost savings
• Business value
• Complexity
• Risk
• Oversee the design, development, testing, deployment, and monitoring of automation workflows.
• Integrate AI models and AI agents into automated business processes.
• Enable intelligent decision-making within automated workflows.
• Establish governance, monitoring, reliability, and support standards for automation solutions.
• Collaborate with business teams to streamline processes and reduce manual effort.

8. AI Governance & Responsible AI

• Establish enterprise policies, standards, and governance frameworks for AI and automation.
• Ensure AI solutions comply with:
• Enterprise security requirements
• Privacy requirements
• Data governance policies
• Regulatory requirements
• Compliance standards
• Establish AI model evaluation and validation frameworks.
• Define standards for measuring:
• Accuracy
• Reliability
• Performance
• Bias
• Safety
• Explainability
• Maintain transparency and auditability of AI-driven decisions.
• Establish appropriate controls for AI model lifecycle management.
• Partner with security, privacy, legal, compliance, and data governance teams as necessary.
• Promote responsible and ethical use of AI across the organization.

9. AI Security & Risk Management

• Ensure enterprise AI systems are designed with appropriate security controls.
• Establish secure architecture patterns for AI applications, agents, models, APIs, and data.
• Address risks associated with:
• Sensitive data
• Data privacy
• Model security
• Prompt injection
• Unauthorized access
• Data leakage
• AI hallucinations
• Model misuse
• Implement appropriate authentication, authorization, access control, monitoring, and audit mechanisms.
• Establish standards for secure AI development and deployment.

10. Cross-Functional Collaboration

• Partner closely with Data Engineering teams to leverage curated enterprise data.
• Work with Reporting and Analytics teams to incorporate AI outputs into business insights and decision-support tools.
• Collaborate with business stakeholders to translate operational challenges into AI-enabled solutions.
• Work with IT and enterprise architecture teams to integrate AI into existing technology platforms.
• Collaborate with business leaders to identify and prioritize AI opportunities.
• Communicate AI capabilities, risks, limitations, timelines, and business value to both technical and non-technical stakeholders.
• Keep stakeholders informed regarding project progress, risks, dependencies, and delivery timelines.

11. Enterprise Application Integration

Experience integrating AI and automation capabilities with enterprise applications is highly valuable.

The role may involve integration with platforms such as:

• ERP systems
• CRM systems
• Logistics platforms
• Business workflow systems
• Enterprise APIs
• Data platforms
• Reporting and analytics systems

The Director will establish scalable integration patterns that allow AI agents, automation workflows, and enterprise applications to work together securely and reliably.

12. Team Development & Organizational Leadership

• Recruit, build, and develop a strong AI/ML and automation organization.
• Mentor engineers, architects, and technical professionals.
• Establish individual and team development goals.
• Promote innovation, collaboration, accountability, and continuous improvement.
• Establish career development and technical growth opportunities.
• Build a culture of engineering excellence and responsible AI adoption.
• Manage priorities, resources, project timelines, and technical dependencies.
• Provide executive-level communication regarding AI strategy, investment, progress, risks, and outcomes.

Required Education

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field.

Equivalent Experience: Four (4) years of relevant professional experience may be considered in lieu of a bachelor's degree.

Advanced degrees in AI, Machine Learning, Computer Science, Data Science, Engineering, or related disciplines are a plus.

Required Experience

• 10+ years of experience in Artificial Intelligence, Machine Learning, automation, data/AI engineering, or related technical disciplines.
• 5+ years of experience leading and managing technical teams.
• Demonstrated experience designing enterprise AI architectures.
• Experience delivering production AI/ML solutions.
• Experience with Generative AI and LLM-based solutions.
• Experience designing or implementing agent-based or multi-agent AI systems.
• Experience with RAG architectures and enterprise knowledge retrieval.
• Experience with modern AI/ML platforms and model lifecycle management.
• Experience working with cross-functional technical and business teams.
• Demonstrated ability to translate business requirements into scalable AI and automation solutions.

Preferred Experience

• 12–15+ years of progressive experience in AI engineering, machine learning engineering, intelligent automation, enterprise data platforms, or related areas.
• Experience leading enterprise AI transformation initiatives.
• Experience with Databricks AI/Mosaic AI.
• Experience with MLflow and model serving.
• Experience with Microsoft Copilot.
• Experience with Microsoft Power Automate/RPA.
• Experience implementing Agentic AI and multi-agent architectures.
• Experience implementing enterprise RAG solutions.
• Experience with vector databases and semantic AI architectures.
• Experience integrating AI and automation with ERP, CRM, logistics, or other enterprise platforms.
• Experience with conversational AI.
• Experience with AI-based image/video processing technologies.

Required Skills

• Strong leadership and team management capabilities.
• Expert-level enterprise AI architecture skills.
• Strong understanding of AI/ML engineering practices.
• Strong Generative AI and LLM expertise.
• Experience with Agentic AI and multi-agent systems.
• Experience with RAG architectures.
• Experience with Databricks AI capabilities.
• Experience with MLflow and model lifecycle management.
• Understanding of vector search, embeddings, and semantic retrieval.
• Strong strategic thinking and problem-solving abilities.
• Strong business acumen and ability to identify high-value AI opportunities.
• Excellent verbal and written communication skills.
• Ability to communicate complex technical concepts to executive and non-technical audiences.
• Ability to manage multiple enterprise initiatives and priorities.

Ideal Candidate

The ideal candidate is a strategic AI technology leader and hands-on technical architect who can bridge enterprise strategy and engineering execution.

This person should be equally comfortable discussing AI strategy with executive leadership, designing an enterprise Agentic AI/RAG architecture with architects and engineers, and identifying opportunities to automate business processes using Databricks, Microsoft Copilot, Power Automate, Generative AI, and intelligent automation.

The successful candidate will bring a combination of deep AI/ML technical expertise, enterprise architecture experience, strong people leadership, business acumen, and a proven ability to transform emerging AI technologies into secure, scalable, production-ready enterprise solutions.