The ideal candidate is a strong Linux, data, database, and automation engineer who can work effectively in complex semiconductor engineering environments. Prior experience with semiconductor engineering domains such as silicon design, DFT, validation, diagnostics, or yield is strongly preferred but not required for candidates with strong automation and data engineering depth.
Minimum Qualifications
- Bachelor's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 4+ years of Hardware Engineering or related work experience.
- OR
- Master's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 3+ years of Hardware Engineering or related work experience.
- OR
- PhD in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 2+ years of Hardware Engineering or related work experience.
Automation Development & Production Engineering
Design, develop, test, and maintain automation tools that improve silicon and system yield engineering workflows, including data processing, diagnostics, reporting, and operation efficiency.
Build reliable Linux-based automation services, scripts, data loaders, and backend systems used by engineering teams.
Develop production-grade solutions with appropriate logging, monitoring, error handling, alerting, documentation, and operational supportability.
Contribute to systems that are maintainable, fault-tolerant, secure, and suitable for long-running production use.
Debug and resolve automation issues across Linux servers, databases, file systems, scheduled jobs, data feeds, and dependent engineering systems.
Work under architectural guidance to implement new automation capabilities while developing deeper ownership of specific tools, workflows, or data domains over time.
Data Systems & Database Automation
Build and maintain data pipelines that collect, transform, validate, and load engineering and manufacturing data across design, DFT, validation, diagnostics, and yield-related systems.
Work with relational databases, SQL, schema design, query optimization, and database-backed automation workflows.
Integrate data from multiple sources, including engineering systems, manufacturing data feeds, logs, reports, and analysis outputs.
Develop tools that make complex data easier to access, validate, monitor, and act upon.
Ensure data quality, traceability, and operational robustness in automation that supports engineering analysis and decision-making.
AI-Assisted Engineering & Automation Acceleration
Use LLM-based coding agents and AI-assisted automation tools responsibly as part of day-to-day engineering work.
Apply AI-assisted development practices to improve code quality, accelerate troubleshooting, generate tests, improve documentation, and increase engineering output.
Build or integrate AI-enabled engineering tools where appropriate, including automation agents, internal assistants, MCP servers, and workflow accelerators.
Maintain strong human review and engineering judgment for AI-generated code, SQL, analysis, documentation, and operational recommendations.
Ensure AI-assisted workflows follow appropriate standards for security, correctness, maintainability, and production safety.
Help establish practical patterns for using AI-first development methods in production engineering environments.
Tooling, Applications & Workflow Integration
Build backend tools, dashboards, web applications, APIs, command-line utilities, and scheduled automation to support engineering needs.
Use appropriate technologies for the problem, including Python, shell scripting, SQL, Django or similar frameworks, REST APIs, log analytics tools, and enterprise data systems.
Integrate automation into existing engineering workflows without disrupting production operations.
Collaborate with engineers and stakeholders to identify manual pain points and convert them into reliable automated solutions.
Create clear documentation, usage guidance, and operational notes so tools can be supported and extended over time.
Cross-Functional Collaboration
Work closely with engineers across design, DFT, validation, diagnostics, and data domains to understand requirements and deliver practical solutions.
Communicate technical findings, implementation options, risks, and tradeoffs clearly to both software-oriented and semiconductor engineering audiences.
Participate in code reviews, design discussions, troubleshooting sessions, and production readiness reviews.
Collaborate effectively across time zones, including regular overlap with U.S. Central Time morning hours.
Contribute to a growing team culture focused on engineering rigor, accountability, automation leverage, and continuous improvement.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Engineering, Software Engineering, or related field, or equivalent practical experience.
Strong hands-on experience with Linux-based development and automation, including shell scripting, server-side troubleshooting, cron or scheduled jobs, file systems, permissions, and operational debugging.
Strong programming skills in Python or a comparable language used for automation, data processing, backend systems, or engineering tools.
Strong experience with SQL, relational databases, data modeling, query development, and database-backed applications.
Experience building automation that processes, validates, transforms, or integrates large technical datasets.
Experience developing production-quality software or automation with testing, logging, error handling, documentation, and maintainability.
Experience using Git or similar version control systems in a collaborative environment.
Practical experience using LLM-based coding agents or AI-assisted development tools to improve productivity.
Ability to review AI-generated work critically and maintain ownership of correctness, security, reliability, and production readiness.
Strong debugging and problem-solving skills across software, data, database, and Linux environments.
Excellent written and verbal communication skills in English.
Ability to work effectively with U.S.-based stakeholders with required time overlap.
Additional consideration
Candidates with strong software, data, or automation backgrounds from non-semiconductor domains will be considered if they demonstrate the ability to learn complex engineering workflows quickly.
Preferred Qualifications
Experience with semiconductor design, DFT, validation, diagnostics, yield, or product engineering environments.
Experience working with engineering datasets such as test data, DFT outputs, manufacturing data, diagnostic data, or high-volume silicon or system telemetry.
Familiarity with semiconductor data formats such as STDF, KLARF, wafer maps, lot history, test results, binning, dispositions, or assembly data.
Experience building automation for engineering teams in semiconductor, manufacturing, hardware, or similar domains.
Experience with Oracle, PL/SQL, ODBC, database loaders, schema design, or production database integration.
Experience with Django or similar backend frameworks.
Experience with REST APIs, internal tools, dashboards, reporting systems, or workflow applications.
Experience with log analytics, monitoring, Splunk, or observability tools.
Experience with batch compute, distributed jobs, LSF or grid environments, or multi-server automation.
Experience building or integrating AI-assisted tools, internal agents, MCP servers, or RAG workflows.
Familiarity with responsible AI-assisted development practices, including human-in-the-loop review and secure handling of generated outputs.
Experience working across global teams and time zones.
Desired Technical Profile
The ideal candidate operates across multiple layers of the engineering stack:
Linux: Comfortable diagnosing runtime issues, managing scripts, and troubleshooting production systems.
Data: Able to handle complex, messy engineering data and build reliable transformations with traceability.
Databases: Strong SQL skills with practical understanding of schemas, performance, and data integrity.
Automation: Able to convert manual workflows into robust and scalable systems.
Production Mindset: Builds systems that are observable, maintainable, secure, and resilient.
AI-First Engineering: Uses AI tools to increase output while maintaining full accountability.
Engineering Communication: Works effectively with domain experts and delivers practical solutions.
What Success Looks Like
A successful engineer in this role will
Deliver reliable automation that reduces manual effort for silicon and systems yield engineering teams across design, DFT, diagnostics, and yield workflows.
Build data tools and pipelines that engineers trust and reuse.
Improve development velocity and quality through responsible use of AI-assisted tools.
Contribute production-ready systems that scale across teams and time zones.
Learn semiconductor workflows well enough to translate engineering needs into practical solutions.
Operate as a strong senior individual contributor while growing into deeper ownership.
Help establish the foundation for a larger Bangalore-based automation capability.
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