AI Quality Assurance (QA) Engineer

Qualcomm

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Bengaluru, Karnataka, India (On-site)2 weeks ago

LocationBengaluru, Karnataka, India (On-site)

QualificationBachelor’s degree in Engineering, CS, or Information Systems

Job Description

As a Qualcomm AI QA Engineer, you will design, develop, and validate embedded and cloud edge software that launches cutting-edge, world-class AI products. You will move far beyond traditional automation scripts to deeply validate the underlying logic, safety, and reliability of complex intelligent systems. Operating at massive scale, you will help solve complex challenges related to ultra-low latency, high-throughput scalability, and the seamless orchestration of on-premises and cloud-based data systems.
You will collaborate heavily with systems, hardware, architecture, and test engineers to design robust system-level validation solutions. Your daily workflows will ensure that Qualcomm’s custom AI solutions, Application APIs, and Model SDKs exceed stringent enterprise performance requirements.

Redefine AI Testing: Move beyond traditional automation to validate the core logic, ethical safety, and output reliability of Generative AI systems.
SDK & API Validation: Rigorously test and validate Model SDKs, Frameworks, and Application APIs that empower internal teams to build custom AI solutions.
Performance Benchmarking: Evaluate AI models and cloud edge software for ultra-low latency and high-throughput scalability.
System Orchestration: Test the seamless orchestration and data pipeline logic between on-premises infrastructure and cloud-based data systems.
Cross-Functional Collaboration: Partner directly with systems, hardware, and architecture engineers to gather performance requirements and design system-level test solutions.
Quality Automation: Create, modify, and validate specialized utility programs to automate the QA lifecycle for SOTA agentic applications.

Education: Minimum Bachelor’s degree in Engineering, Information Systems, Computer Science, or a closely related technical field.
AI Literacy: Strong foundational understanding of Generative AI, Large Language Models (LLMs), and agentic application architectures.
Testing Expertise: Proven experience in software quality assurance, API testing, and writing robust automation scripts (Python highly preferred).
Performance Mindset: Familiarity with testing highly scalable systems, cloud/edge integrations, and latency benchmarking.
Problem-Solving: Strong analytical skills required to debug complex intelligent systems and identify edge-case logic failures.
Team Collaboration: Excellent communication skills to interface with diverse engineering groups and drive product excellence.

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