Overview

Qualcomm India Private Limited

Responsibilities

• This position is part of a broader effort to increase automation capacity within the silicon engineering organization, beginning with new engineering roles in the Bangalore Design Center (BDC). The engineer will work closely with U.S.-based yield automation leadership and stakeholders, including regular overlap with U.S. Central Time morning hours.
• 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.

Qualifications

• Bachelor's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 4+ years of Hardware Engineering or related work experience.
• 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.
• 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.
• 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.
• 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.
• 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.
• Master's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 3+ years of Hardware Engineering or related work experience.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Engineering, Software Engineering, or related field, or equivalent practical experience.