Principal AI​/Machine Learning Engineer

Principal AI / Machine Learning Engineer

Position at ZT Systems.

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, acting 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

• 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…