Principal Engineer, Data, BIA & AI - Architect

• Minimum Degree in data science, information technology, computer science, mathematics, engineering, business administration, or a related field.
• Minimum 7 years of experience in enterprise, solution, data, cloud or comparable architecture roles, including ownership of complex end-to-end designs.
• Minimum 2 years of practical experience designing and delivering AI-enabled business solutions, Generative AI, Copilots, automation or Agentic AI.
• Strong understanding of enterprise AI solution patterns, including model and service selection, Copilots, RAG, embeddings, agents, orchestration, evaluation, integration, observability and operationalization; sufficient model knowledge to assess trade-offs without requiring a research or model-development background.
• Hands-on solutioning experience with APIs, cloud AI services, enterprise integration, workflow automation, prototyping tools, source control, automated testing and CI/CD; the ability to use Python or comparable languages to validate concepts is beneficial.
• Ability to translate process and business outcomes into scalable, operable AI solutions and guide teams from exploration through production and adoption.
• Good understanding of responsible AI, and AI governance.
• Hands-on experience with cloud platforms such as Microsoft Azure, AWS or Google Cloud, and familiarity with modern data and AI platforms.
• A strong drive to understand ASM’s business operations, semiconductor equipment, end-to-end processes and commercial products.
• Action-oriented and pragmatic, able to move quickly, experiment intelligently and simplify complexity while maintaining sound architecture discipline.
• Excellent stakeholder engagement and communication skills, with the ability to explain complex AI choices and trade-offs to business leaders and technical teams.
• Outcome-driven mindset focused on adoption, speed to value, scalability, resilience, cost efficiency and measurable impact.

• Define ASM’s enterprise AI target architecture, roadmap, standards and reusable solution patterns.
• Define how Databricks, Microsoft Copilot, SAP Joule, specialized AI tools and other capabilities fit together, balancing enterprise platforms with justified domain-specific needs.
• Design modular, interoperable AI services that can be reused across products and functions, and guide make, buy, integrate and retire decisions.
• Establish and maintain an enterprise AI inventory.
• Lead AI architecture reviews and maintain alignment with Enterprise Architecture, the Architecture Review Board and ASM’s broader technology roadmap.

• Work with Business Process Data Owners, Digital Transformation Business Partners and other teams to identify where AI, automation and process redesign can remove friction, increase capacity and deliver measurable outcomes.
• Assess process and data readiness for each AI value case, including ownership, standardization, data quality, security and suitability of data.
• Advise when to use rules-based automation, Microsoft Copilot, SAP Joule, specialized AI products, AI agents, retrieval-augmented generation, traditional machine learning, or commercially available small, large or multimodal models based on business fit, accuracy, latency, risk and cost.
• Translate opportunities into practical solution designs, prototypes and proofs of value; work with AI Engineers, Data Engineers, Data Scientists and other teams to configure AI services, connect enterprise data and systems, validate assumptions and accelerate deployment.
• Define reference architectures for AI-enabled solutions, covering Copilots, agent frameworks, retrieval-augmented generation, workflow orchestration, APIs, data access, human oversight and integration with ASM’s digital landscape.
• Define production standards for AI solutions covering source control, automated testing and evaluation, prompt and configuration management, CI/CD, solution inventories and registries, deployment, rollback, observability, reliability, service ownership and operational support.
• Design for cost at scale through model routing, token budgets, caching, prompt and context optimization, workload sizing, consumption monitoring and FinOps controls.

• Partner with the AI Governance and Cyber Security to build responsible AI, privacy, intellectual property, Trade Compliance and ethical requirements into solutions from the start.
• Define practical Agentic AI boundaries, including permitted data and actions, identity and least-privilege access, human approval points, segregation of duties, fail-safe behavior, audit trails and escalation.

• Work as part of multidisciplinary teams with business and process owners, AI Engineers, Network Engineers, Data Engineers, Data Scientists, the Data Architect, Cloud and others.
• Play an active role in ASM’s Innovation Lab, rapidly testing AI products, available models, Copilots, agent patterns, automation tools and platforms, learning from failure and turning successful experiments into repeatable production solutions.
• Help shape and prioritize AI opportunities across ASM from sales, engineering, manufacturing, to services.
• Create practical architecture guidance, reference implementations and reusable building blocks, and coach teams by working with them rather than advising from a distance.

• Experience with Databricks, Microsoft Copilot or Copilot Studio, SAP Joule, Azure AI, AWS AI/ML, Google Cloud AI or comparable enterprise AI platforms.
• Experience working in a semiconductor, engineering, or high-tech manufacturing environment.

Apply today to be part of what’s next.

We make the tech that enables the chips in devices which improve lives around the world. We do this with an eye to the future, pushing the boundaries of what’s possible through cutting-edge innovation, and driving the next wave of technological breakthroughs that shape how we live, work, and connect.

To learn more about ASM, find us at asm.com and on LinkedIn, Facebook, Instagram, X and YouTube.