Ai & Automation Lead -Senior Staff Engineer

Job Summary

Company: Qualcomm India Private Limited

Job Area: Engineering Group, Engineering Group > Software Engineering

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.

As a leading technology innovator, Qualcomm pushes the boundaries of what is possible to enable next-generation experiences and drive digital transformation. We are seeking a Senior Staff Engineer to lead the adoption of Artificial Intelligence and intelligent automation within Software Quality Engineering.

In this role, you will define and drive the strategy for AI-powered validation across firmware, platform, and system-level software. You will architect and scale next-generation quality platforms that leverage Generative AI, autonomous agents, data-driven quality intelligence, and advanced automation to fundamentally improve how software is validated, analyzed, and released.

This is a highly visible technical leadership position that combines deep expertise in software quality engineering with a passion for applying emerging AI technologies to solve complex engineering challenges. You will work across engineering, product, and infrastructure teams to modernize validation practices and improve product quality across multiple programs and product generations.

Requirements

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.

OR Master\'s degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience.

OR PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.

3+ years of work experience with programming languages such as C, C++, Java, Python, etc.

Key Responsibilities

AI-Powered Quality Engineering: Define and execute the roadmap for AI adoption within Software Quality Engineering.

AI-Powered Quality Engineering: Drive the use of Generative AI and autonomous systems to improve validation coverage, engineering productivity, defect discovery, and release confidence.

AI-Powered Quality Engineering: Establish best practices for AI-assisted test development, validation workflows, and engineering automation.

AI-Powered Quality Engineering: Identify opportunities to transform traditional validation processes through AI-driven solutions.

Autonomous Validation Systems: Design and develop intelligent validation systems capable of generating, executing, analyzing, and maintaining test assets with minimal manual intervention.

Autonomous Validation Systems: Leverage AI to automate test development, exploratory testing, coverage analysis, regression management, and defect triage.

Autonomous Validation Systems: Develop scalable validation solutions that support complex software and firmware ecosystems across multiple product generations.

Autonomous Validation Systems: Evaluate and integrate emerging AI technologies that deliver measurable engineering and quality benefits.

Quality Intelligence Analytics: Build AI-driven capabilities for failure triage, defect clustering, anomaly detection, quality trend analysis, and validation risk assessment.

Quality Intelligence Analytics: Develop intelligent systems that leverage logs, telemetry, crash data, and execution history to improve engineering decision-making and accelerate root-cause identification.

Quality Intelligence Analytics: Establish meaningful quality metrics and actionable insights that improve validation effectiveness and release readiness.

Test Infrastructure Platform Architecture: Define the architecture and evolution of automation frameworks, validation platforms, and quality engineering infrastructure.

Test Infrastructure Platform Architecture: Integrate AI-powered validation workflows into CI/CD environments to deliver continuous quality feedback throughout the development lifecycle.

Test Infrastructure Platform Architecture: Drive scalable automation solutions supporting functional, integration, performance, stress, stability, and system-level validation across multiple programs, architectures, and hardware generations.

Technical Leadership Innovation: Serve as a technical leader for AI and automation initiatives across the broader quality engineering organization.