Director, AI Integration

Responsibilities

• Strategic AI Roadmap
• Define and collaborate with the CEO to execute a multi-year AI strategy aligned with DWFritz Automation’s business objectives.
• Identify market trends, emerging technologies, and customer requirements to steer AI initiatives that maintain a competitive advantage.
• Solution Architecture and Integration
• Develop end-to-end AI architectures (data pipelines, ML models, and deployment environments) for industrial automation systems.
• Coordinate with engineering, software, and controls teams to embed AI/ML models seamlessly into production lines, robotics platforms, machine vision solutions, and quality control systems.
• Ensure robust integration methods, including on-premises, edge, or cloud-based AI, aligned with manufacturing constraints and uptime requirements.
• Technology Leadership
• Provide expert guidance on model selection (machine learning, deep learning, reinforcement learning, generative AI, etc.) based on project requirements and constraints.
• Evaluate and deploy advanced data acquisition and analytics approaches, such as real-time anomaly detection, predictive maintenance, and digital twin modeling.
• Maintain an up-to-date perspective on the latest AI frameworks, platforms, and development best practices (e.g., PyTorch, TensorFlow, ONNX, MLOps pipelines).
• Cross-Functional Collaboration
• Working closely with sales, product management, and engineering leads to translate market opportunities into AI-focused solutions.
• Coordinate with operations teams on implementation feasibility, deployment timelines, and resource allocation.
• Engage with customer-facing teams to communicate project deliverables and performance metrics effectively.
• Team Building and Mentorship
• Recruit, develop, and lead a high-performing AI team of data scientists, ML engineers, and software developers.
• Foster a culture of innovation, collaboration, and continuous learning, setting the groundwork for scalable AI solutions.
• Provide ongoing training, knowledge sharing, and mentorship to enhance the broader organization’s AI fluency.
• Project Management and Delivery
• Direct multiple AI/ML projects concurrently, ensuring technical milestones, budgets, and timelines are met.
• Implement best practices for model performance monitoring, version control, and iterative improvements (CI/CD, MLOps).
• Establish clear metrics for success—deployment performance, accuracy, cost savings, or quality improvements—and ensure consistent reporting.
• Compliance and Risk Management
• Ensure adherence to relevant industry standards and regulations (e.g., ISO, Automotive SPICE, functional safety considerations).
• Collaborate with legal and compliance teams to address data privacy, security, and intellectual property rights in AI-driven applications.
• Mitigate model and data-related risks through robust validation, testing, and fallback mechanisms.
• Partnerships and Innovation
• Forge strategic partnerships with AI technology vendors, research institutions, and industry consortia to accelerate innovation.
• Champion continuous research & development efforts, pilot emerging AI techniques, and drive patent or trade secret strategies where relevant.

• Identify and develop new revenue streams or market segments leveraging cutting-edge AI solutions

Knowledge, Skills and Abilities

• Industry Knowledge:
• Comprehensive understanding of manufacturing processes, plant floor operations, and regulatory standards.
• Awareness of automotive manufacturing trends, including EV production lines, advanced robotics, and digital factory strategies.
• Soft Skills:
• Exceptional communication and presentation skills, capable of articulating complex AI concepts to both technical and non-technical audiences.
• Adept collaborator, comfortable engaging with C-level executives, engineering leaders, and external stakeholders.

• Predictive and Prescriptive Maintenance
• Leverage Machine Learning to predict failures, schedule maintenance, and reduce unplanned downtime.
• Integrate real-time analytics for advanced fault detection and dynamic system re-configuration.
• Generative AI for Process Optimization
• Investigate generative models (e.g., GPT-based systems) to simulate production workflows, reduce cycle times, and optimize resource usage.
• Explore AI-driven anomaly detection in both structured and unstructured sensor data.
• Digital Twins and Virtual Commissioning
• Develop digital twin models to simulate and refine manufacturing cells before physical implementation, improving design efficiency and reducing commissioning time.
• Integrate AI-based feedback loops into digital twins for robust performance monitoring and continuous improvement.
• Edge Computing and Real-time Decision Making
• Implement efficient AI inference on embedded or edge devices to support real-time control, meeting low-latency requirements in high-speed manufacturing.
• Evaluate next-generation hardware accelerators (GPUs, TPUs, FPGAs) for high-performance computing in factory environments.
• Scalable MLOps Framework
• Establish continuous integration/continuous deployment (CI/CD) pipelines tailored to the manufacturing context, ensuring reliable and iterative improvements to production models.
• Standardized data collection, labeling, model deployment, and monitoring for consistent, high-quality outcomes across projects.

Education and Experience

• Bachelor’s or master’s degree in computer science, Electrical Engineering, Robotics, or a closely related field. A PhD is advantageous but not mandatory.
• 10 years of progressively responsible experience in AI/ML roles, ideally within industrial automation, advanced manufacturing, or automotive sectors.
• Proven track record of designing and deploying complex AI systems: from data ingestion and feature engineering to real-time model serving.
• Deep proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and programming languages (Python, C , etc.).
• Familiarity with industrial communication protocols, PLCs, SCADA systems, edge computing paradigms, IoT sensors, and machine vision tools.
• Previous experience building and managing AI-focused teams, including performance management, hiring, and mentorship.
• Successful delivery of large-scale AI integration projects across multiple sites or clients.
• Strong project management acumen: scope definition, scheduling, budgeting, risk analysis, and stakeholder communication.

Interpersonal Contacts

We are an ITAR protected facility and due to the nature of your role, you may encounter ITAR related project information. Your citizenship status will determine what access you have at the facility.

External to DWFritz

Vendors, research institutions, and industry consortia.

Internal to DWFritz

The most common internal contacts are with engineering, management, project managers, and administrative staff.

Work Environment

Onsite requirement.

Physical Demands

The physical demands listed here are typical for the role and may be modified upon request for reasonable accommodation.  The position requires the employee to communicate with others including talking and hearing, sometimes in environments with significant ambient noise. The employee must be able to wear personal protective equipment and gear.  The employee must be able to ascend and descend ladders, work in confined spaces, and be mobile / working on their feet for much of the day. The employee may occasionally lift up to 25 pounds; bend, stoop, kneel, and grasp. The employee may be working at a personal computer workstation for most of the workday.

Equal Opportunity Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.