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# Automation lead with Python and GCP

- **Company:** Cognizant Technology Solutions Corporation
- **Location:** Goodlettsville, TN, United States
- **Experience:** Expert
- **Salary:** $90,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Continuous Integration, DevOps, Python (Programming Language), Machine Learning, Performance Tuning, Release Management, Software Engineering, Strategies of Testing, Google Cloud, Spring Cloud, System Availability, Large Language Models, Generative AI, AI Platforms, Kubernetes, Virtual Agents, Devsecops, SDET, Microservices
- **Published:** August 1, 2026
- **Apply:** https://dejobs.org/x/x/8B3B714CEB7244E69F88AFABE23CAEF1/job/

## About the Role

* 7 to 9 years of experience in Software Development Engineering in Test (SDET), Quality Engineering, or Automation Engineering.
 * Strong expertise in Python development and automation framework design.
 * Hands-on experience with AI/ML, Generative AI, Agentic AI, and Large Language Model (LLM)-based systems.
 * Strong knowledge of Google Cloud Platform (GCP), including Kubernetes, Cloud Run, and Vertex AI.
 * Experience building scalable cloud-native applications and microservices architectures.
 * Proven experience with CI/CD pipeline implementation, DevOps, and DevSecOps practices.
 * Strong understanding of test automation, validation frameworks, platform reliability, security, scalability, and performance optimization.

## Description

· Develop scalable Quality Engineering Automation (QEA) platforms supporting AI/LLM applications and agentic workflows.

· Design, build, and maintain Python-based automation frameworks, APIs, and platform services.

· Develop and deploy cloud-native AI applications using GCP services such as Kubernetes, Cloud Run, and Vertex AI.

· Create and implement AI model evaluation, testing, and validation frameworks to ensure quality and reliability.

· Build and operationalize CI/CD pipelines for AI platforms, model deployments, and application releases.

· Implement observability, monitoring, tracing, security, and governance practices across AI platforms.

 * Collaborate with Product, Data Science, and Quality Engineering teams to establish continuous validation strategies.


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