Principal AI​/Machine Learning Engineer

Position: 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…