Software Engineer II, AI Apps and Cloud Infrastructure
New
J
JobgetherAI and Robotics
Based in United StatesFull-TimeMiddle
Salary137,376 - 146,000 USD per year
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Job Details
- Experience
- 2+ years
- Required Skills
- DockerPythonGCPGitKubernetesTypeScriptGoRESTful APIs
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
- 2+ years of professional software development experience.
- Hands-on experience designing, developing, and maintaining cloud infrastructure, preferably with Google Cloud Platform (GCP).
- Proficiency in programming languages such as Go, Python, or TypeScript, with the ability to write clean, maintainable code.
- Experience using version control systems such as Git.
- Familiarity with cloud infrastructure concepts including computing, storage, networking, and scalable services.
- Experience with infrastructure-as-code tools such as Pulumi or similar technologies.
- Knowledge of containerization technologies including Docker and orchestration platforms such as Kubernetes.
- Understanding of RESTful API design and implementation.
- Experience building scalable data workflows, pipelines, or data engineering solutions.
- Experience using GenAI tools and coding assistants to improve development speed, code quality, and engineering efficiency.
Responsibilities
- Develop and maintain scalable, reliable cloud infrastructure using Google Cloud Platform (GCP).
- Design and implement cloud services that support AI applications, data workflows, and robotic technology platforms.
- Collaborate with machine learning and research teams to ensure efficient data processing, quality, and system performance.
- Build APIs and backend services that enable seamless integration between cloud platforms, web applications, and robotic systems.
- Work closely with application teams to understand technical requirements and provide engineering guidance.
- Participate in unit, integration, and system testing to ensure software reliability, scalability, and quality.
- Monitor and optimize cloud costs, performance, availability, and overall platform reliability.
- Identify technical issues, troubleshoot bottlenecks, and implement improvements to enhance system efficiency.
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