# Senior AI Engineer for QA and SDLC at Procter & Gamble

Procter & Gamble is hiring a Senior AI Engineer for QA and SDLC. Level rates it Works on AI ●●●○; you can [apply on Procter & Gamble](https://jobsbylevel.com/go/56280597-decf-43fd-b903-774fc91e475d?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. WARSAW PLANT & GO.

## Details

- Company: [Procter & Gamble](https://jobsbylevel.com/companies/procter-gamble)
- AI level: AI Level 3 (score 65 out of 100)
- Location: WARSAW PLANT & GO
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/56280597-decf-43fd-b903-774fc91e475d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-ai-engineer-for-qa-and-sdlc-at-procter-gamble-d4907b

## Prepare for this job

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## Description

Job Location WARSAW PLANT & GO Job Description As the Senior AI Engineer for QA and SDLC at P&G, you will lead the transformation of software quality assurrance across critical IT products and transformations around Digital Marketing and Commerce. You will define and build the next generation of quality engineering capabilities by combining AI engineering, agentic automation, intelligent testing, DevOps, software engineering, and product management . You will explore and implement AI agents and automation capabilities that improve how software is validated, released, monitored, and continuously optimized. Your work will help teams move from manual and reactive testing toward AI-enabled, continuously learning quality engineering . You will operate at the center of a large-scale P&G transformation, partnering with engineering, architecture, product, and DevOps teams. The role offers an opportunity to work with cutting-edge technologies, shape enterprise-wide engineering standards, and establish reusable AI capabilities that can improve software quality at scale as well as speed to market. Job Responsibilities Lead AI-driven quality transformation Define and execute the vision and roadmap for AI-enabled Quality Engineering across IT products and platforms. Transform traditional quality assurance into a proactive, automated, and engineering-led discipline embedded throughout the software development lifecycle. Identify high-value opportunities for AI agents, generative AI, machine learning, and intelligent automation to improve test design, execution, analysis, and defect prevention. Collaborate with P&G Horizontal DevOps and QA Communities of Practice to apply best practices and contribute to company wide practices and tools. Build agentic QA capabilities Re-apply, design and implement AI-assisted and agentic capabilities for QA activities across SDLC - in particular: orchestration for test scope definition, test execution, test data generation, regression impact assessment, release readiness evaluation. Engineer automation at scale Define and lead implementation of scalable test-automation strategies covering functional, integration, API, performance, security, accessibility, and end-to-end testing. Lead the modernization of test frameworks and integrate automated quality controls directly into CI/CD pipelines. Reduce manual testing through risk-based automation, intelligent test selection, reusable frameworks, and self-service engineering capabilities. Drive quality earlier in the lifecycle through shift-left practices, automated design validation, contract testing, static analysis, and continuous verification. Lead people and capability development Lead team of quality engineers and automation specialists to execute the QA transformation and support product roadmaps inline with business priorities. Job Qualifications Demonstrated experience defining and implementing quality-engineering strategies for complex enterprise applications and platforms. Software-engineering background, including practical programming experience in one or more languages such as Python, Java, C#, JavaScript, or TypeScript. Nice to have: Experience designing and implementing test-automation frameworks using technologies such as Playwright, Cypress, Selenium, pytest, JUnit, NUnit, or comparable tools. Strong understanding of AI engineering concepts, including generative AI, large language models, prompt engineering, tool-enabled agents, evaluation methods, and AI application lifecycle management. Experience applying AI or intelligent automation to software engineering, testing, DevOps, operations, or adjacent enterprise processes. Practical knowledge of DevOps practices, including CI/CD, infrastructure as code, automated deployment, environment management, observability, and production monitoring. Experience with functional, API, integration, end-to-end, performance, security, accessibility, and user-acceptance testing. Understanding of modern

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