What You’ll Do
Understand and Automate
Engage directly with operational and engineering stakeholders to understand, map, and automate complex business processes — translating domain expertise into reliable, scalable software
Analyze existing workflows across manufacturing and quality operations to identify automation opportunities with measurable impact on productivity, cycle time, and decision quality
Design and implement end-to-end automated workflow solutions that reduce manual effort and increase operational consistency
Build with Engineering Excellence
Build scalable, maintainable software with clean architecture, disciplined testing, and rigorous version control practices
Design and implement agentic automation solutions leveraging emerging frameworks such as Model Context Protocol (MCP) and agent-to-agent (A2A) protocols to create intelligent, composable workflow systems
Apply strong software engineering fundamentals to ensure solutions are robust, extensible, and production-grade
Lead and Elevate
Define and drive technical strategy for workflow automation — setting engineering standards and identifying platform-level opportunities that compound impact over time
Mentor engineers and elevate team capability through technical guidance, code reviews, and knowledge sharing
Proactively identify and self-initiate improvements beyond assigned scope, bringing a continuous improvement mindset to everything you build
Minimum Requirements
Bachelor’s degree in Computer Science, Computer Engineering, ECE, Electrical Engineering, or related field
5 years of software engineering experience
Proficiency in Python and SQL for automation, data processing, and complex database interactions
Experience with git and modern software development practices including code reviews, testing, and CI/CD
Familiarity with AI-assisted development tools as a productivity accelerator
Preferred Qualifications
Masters degree
Demonstrated ability to understand complex engineering or business processes and translate them into automated, production-grade solutions
Strong foundation in data structures and algorithms
Experience in semiconductor manufacturing or familiarity with fab operations, quality systems, or product lifecycle processes
Familiarity with quality engineering concepts such as defect analysis, yield management, reliability, or corrective action workflows
Experience with MES (Manufacturing Execution Systems), lab data systems, or semiconductor equipment data interfaces
Proven track record of successfully automating multi-step engineering or business workflows at enterprise scale
Hands-on experience with agentic frameworks, MCP, A2A, or LLM integration in production systems
Excellent communication skills for engaging technical and non-technical stakeholders at all levels
