Manager of AI, Tools & Automation

Position Summary

Lead a high-velocity engineering team focused on applying AI, software tools, and automation to transform how Product Validation operates. The successful candidate will identify high-impact opportunities, rapidly deliver solutions, and drive measurable improvements in engineering productivity, execution speed, quality, and scalability.

This role is accountable for outcomes, not activity. Success is measured by business impact, adoption, and acceleration of engineering execution.

Key Responsibilities

• Build and lead a highly capable AI, Tools, and Automation development team.
• Create and execute a roadmap focused on eliminating manual work and accelerating engineering velocity.
• Drive adoption of AI technologies, including LLMs, intelligent agents, workflow automation, and data-driven insights.
• Develop tools and automation that improve validation efficiency, test execution, reporting, knowledge management, and decision making.
• Partner closely with Product Validation leadership to identify bottlenecks and rapidly deploy solutions.
• Foster a culture of experimentation, agility, ownership, and continuous improvement.
• Prioritize execution speed while maintaining quality, reliability, and scalability.
• Continuously measure impact through productivity gains, cycle-time reduction, quality improvements, and user adoption.
• Partner with System Engineering, Program Management, Manufacturing, and other JDS organizations to identify and scale AI, tooling, and automation opportunities.
• Drive adoption of common platforms, workflows, and data solutions that improve efficiency and effectiveness across the broader JDS organization.
• Establish reusable capabilities that can be leveraged across multiple engineering and operational functions.

Qualifications

• BS in Computer Science, Software Engineering, Electrical Engineering, or related field.
• 10+ years of software, automation, data, or AI development experience.
• 5+ years of engineering leadership experience.
• Proven ability to build teams and deliver business-impacting software solutions.
• Strong understanding of AI/LLM technologies, modern software development, automation frameworks, and cloud-based architectures.

Success Measures

• Significant reduction in manual engineering effort.
• Faster validation and product development cycles.
• Broad adoption of AI-enabled workflows and tools.
• Demonstrated improvements in productivity, quality, and organizational effectiveness.
• High-performing team with a reputation for speed, innovation, and delivering measurable results.
• Quantifiable productivity and quality improvements beyond Product Validation.
• Demonstrated impact on engineering execution, operational efficiency, and organizational scalability across JDS.

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