Director AI Integration

Responsibilities

• Strategic AI Roadmap
• Define and collaborate with the CEO to execute a multiyear AI strategy aligned with DWFritz Automations business objectives.
• Identify market trends emerging technologies and customer requirements to steer AI initiatives that maintain a competitive advantage.
• Solution Architecture and Integration
• Develop endtoend 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 onpremises edge or cloudbased 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 realtime anomaly detection predictive maintenance and digital twin modeling.
• Maintain an uptodate perspective on the latest AI frameworks platforms and development best practices (e.g. PyTorch TensorFlow ONNX MLOps pipelines).
• CrossFunctional Collaboration
• Working closely with sales product management and engineering leads to translate market opportunities into AIfocused solutions.
• Coordinate with operations teams on implementation feasibility deployment timelines and resource allocation.
• Engage with customerfacing teams to communicate project deliverables and performance metrics effectively.
• Team Building and Mentorship
• Recruit develop and lead a highperforming 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 organizations 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 successdeployment performance accuracy cost savings or quality improvementsand 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 AIdriven applications.
• Mitigate model and datarelated 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 cuttingedge 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 nontechnical audiences.
• Adept collaborator comfortable engaging with Clevel executives engineering leaders and external stakeholders.

• Predictive and Prescriptive Maintenance
• Leverage Machine Learning to predict failures schedule maintenance and reduce unplanned downtime.
• Integrate realtime analytics for advanced fault detection and dynamic system reconfiguration.
• Generative AI for Process Optimization
• Investigate generative models (e.g. GPTbased systems) to simulate production workflows reduce cycle times and optimize resource usage.
• Explore AIdriven 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 AIbased feedback loops into digital twins for robust performance monitoring and continuous improvement.
• Edge Computing and Realtime Decision Making
• Implement efficient AI inference on embedded or edge devices to support realtime control meeting lowlatency requirements in highspeed manufacturing.
• Evaluate nextgeneration hardware accelerators (GPUs TPUs FPGAs) for highperformance 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 highquality outcomes across projects.

Education and Experience

• Bachelors or masters 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 realtime model serving.
• Deep proficiency in machine learning frameworks (TensorFlow PyTorch scikitlearn 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 AIfocused teams including performance management hiring and mentorship.
• Successful delivery of largescale 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.

Required Experience:

Director