Build Scalable AI Automation Solutions for Next-Gen Automotive Software
Who We Are
The Quality Assurance team is building next-generation AI-driven solutions that transform how automotive software work products are verified and validated. We design intelligent testing frameworks, AI-powered quality analytics, and automated validation workflows that ensure software artifacts meet the highest quality standards across the development lifecycle.
Our focus is on connecting AI-based testing solutions directly to CI/CD pipelines, enabling continuous, scalable, and data-driven quality assurance. These systems automatically assess work products, detect anomalies, and provide actionable insights to development teams, ensuring faster feedback and higher software reliability.
Your Impact
As a Python DevOps Engineer within the Quality Assurance team, you will architect and scale AI-driven validation solutions that automatically test and assess software work products across automotive programs. You’ll design Python-based frameworks, define automation architecture, and integrate advanced AI-powered verification connected to CI/CD pipelines.
Responsibilities
Key Responsibilities
AI Test Architecture, Development & Integration
Design, develop, and maintain AI automation for automotive projects.
Create and maintain AI automation scalable solutions for automotive projects.
Enable and optimize solutions with a focus on efficiency and execution time, ensuring high stability of results.
Configure and operationalize remote execution environments, including authentication, cluster setup, and performance tuning.
Integrate AI automation solutions into Jenkins and GitHub Actions CI/CD pipelines.
Interact with PL team to bring fully compatible AI automation solutions aligned with CI/CD strategy.
Python Automation & Framework Development
Build robust Python frameworks supporting build/test automation, data collection (PostgreSQL, SQL), orchestration, and continuous verification.
Develop modular libraries, CLI tools and interfaces, internal APIs, and automation logic aligned with CI/CD standards.
Engineering Workflow Automation
Automate integration flows across tools, microservices, internal systems, and external vendors.
Improve developer experience by reducing execution time, optimizing caching strategies, and simplifying workflows.
Dashboards, Observability & Reporting
Build dashboards to visualize AI Automation performance, CI/CD metrics, quality indicators, pipeline health, and operational KPIs.
Develop data pipelines to collect, process, and surface telemetry across distributed systems.
Cross-Team Collaboration
Work closely with global software teams, platform architects, and DevOps specialists to align and extend engineering capabilities.
Promote best practices in Python, Bazel, CI/CD, Git workflows, and automation design principles.
Troubleshooting & Continuous Improvement
Diagnose failures, pipeline bottlenecks, misconfigurations, caching issues, and integration conflicts.
Continuously explore improvements such as parallel execution, rule optimization, pipeline refactoring, and new DevOps tools.
