DevTestOps AI Software Developer
About The Position
A hands-on role focused on building and automating DevTestOps infrastructure and processes using GenAI concepts and tools. Deployed internal engineering operational tools are used across the entire engineering group and continuously throughout the entire SDLC process..Detailed DescriptionPerforms tasks such as, but not limited to, the following: Software: Develop software to integrate AI tools into or replace existing engineering operational tools. Software projects include implementing data formatting, developing pipeline infrastructure with well defined APIs, writing machine learning algorithms, and building RAG tools in conjunction with various LLM models. Automation: Investigate third-party tools, conduct proof-of-concepts, develop scripts for automating workflows, and verify robustness of deployments. Cloud Infrastructure: Work with GCP cloud platforms to deploy, manage, scale, and support engineering operational infrastructure. Visibility: Define and develop dashboards to provide visibility into engineering workflows and status of projects Troubleshooting: Resolve issues that arise during development, testing, and production.
Requirements
- Experience with CI/CD and test automation concepts and tools.
- Experience with Google Cloud Platforms (GCP).
- Experience implementing machine learning algorithms using commonly used Python libraries.
- Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Experience with version control systems such as Git.
- Knowledge of Agile methodologies.
- Proven software developer with Python programming experience.
- Technical background associated with automating agentic workflows.
- Excellent communication and problem-solving skills.
- Ability and strong interest associated with investigating new technologies.
- Capacity to quickly react and positively adjust to changing company needs.
Nice To Haves
- Experience with Jira, Confluence, and qTest is a plus.
Responsibilities
- Develop software to integrate AI tools into or replace existing engineering operational tools.
- Implementing data formatting
- Developing pipeline infrastructure with well defined APIs
- Writing machine learning algorithms
- Building RAG tools in conjunction with various LLM models
- Investigate third-party tools
- Conduct proof-of-concepts
- Develop scripts for automating workflows
- Verify robustness of deployments
- Work with GCP cloud platforms to deploy, manage, scale, and support engineering operational infrastructure.
- Define and develop dashboards to provide visibility into engineering workflows and status of projects
- Resolve issues that arise during development, testing, and production.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees
