Principal AI / Machine Learning Engineer

About The Role The Principal AI/Machine Learning Engineer will oversee defining and executing ZT’s roadmap for applying artificial intelligence and machine learning in manufacturing. The AI/ML Transformation Architect will be the pivotal role in shaping ZT’s future-state vision for AI & ML by identifying high-impact use cases, preparing the organization structurally and technically for adoption, and driving successful implementation of applications.

What You Will Do

• Lead or contribute to transformation initiatives, helping set new standards for how ZT approaches manufacturing risk analysis, quality, and continuous improvement.
• Partner with leadership to define the vision and strategy for AI/ML adoption across manufacturing operations.
• Work with factory engineering, quality, and operations to identify, evaluate, and prioritize AI/ML use cases that deliver measurable business value.
• Collaborate across design, quality, manufacturing, test, and supplier engineering to drive solutions that integrate seamlessly into production.
• Define and implement new systems, processes, or frameworks that support the smart factory vision, including automation, metrology, advanced inspection, and predictive analytics.
• Define the organizational, data, and process changes required to prepare the business for AI/ML integration.
• Drive the design, development, and deployment of AI/ML solutions, ensuring successful adoption across factories.
• Apply AI/ML techniques to analyze manufacturing data sets – including metrology, vision inspection, event data, test results – conduct regression analysis, correlation studies, and commonality analysis.
• Leverage deep, data-rich environments and tools (e.g., Minitab, JMP, Python, R, SQL) to generate insights that improve yield, reliability, and throughput.
• Apply advanced statistical and analytical methods (regression, correlation, DOE, SPC, PFMEA, Gauge R&R, commonality studies) to identify, quantify, and control risk in complex manufacturing environments.
• Champion the cultural and operational transformation required for AI/ML success, including training and upskilling the industrial engineering team in new methods and approaches for mathematical computing.
• Serve as the bridge between industrial engineering, factory engineering teams, quality, and IT on AI/ML initiatives.
• Coach and nurture data stakeholders to maximize their potential and facilitate a culture of learning and growth. Act as a thought partner and subject matter expert to refine ideas, generate hypotheses, and analyze data to formulate solutions.
• Demonstrate strong leadership and influence management skills, including the ability to challenge the status quo and manage key senior stakeholders.
• Use predictive analytics to inform PFMEA analyses that will result in actionable process controls, ensuring proactive prevention of variation rather than reactive correction.

What You Bring The right person for this role is an agent of change and has exceptional analytical capabilities, thrives in a fast-paced environment, loves problem-solving, is a good communicator, and is passionate about enabling the future of cloud computing.

• Advanced degree in Engineering, Computer Science, Data Science, or a related field.
• 10–15 years of experience in high-volume, high-complexity manufacturing, with at least 5 years in leadership or transformation roles (not necessarily people management).
• Demonstrated expertise in statistical and analytical methods such as regression analysis, correlation analysis, DOE, SPC, PFMEA, Gauge R&R, and commonality studies.
• Fluency with data-driven tools such as Minitab, JMP, Python, R, SQL (or equivalent) to analyze and interpret large, complex datasets.
• Track record of driving measurable improvements in yield, reliability, or process robustness.
• Background in electronics assembly, PCBA, servers, or other high-reliability industries (e.g., aerospace, medical devices, automotive, etc.).
• Experience with applying AI/ML toolsets to statistical problem solving, predictive analytics, or anomaly detection
• Experience coaching or mentoring technical teams to upskill in statistical methods and data-driven decision-making.
• Strong background in leveraging manufacturing data (metrology, vision systems, event logs, quality data) to build AI/ML-enabled solutions.
• Proven ability to drive organizational changes in data-driven transformations.
• Advanced skills in mathematica…