GenAI Technical Architect

Overview

backend and platform-focused engineer

end-to-end design, development, and deployment

cloud-native architecture and scalable infrastructure

Responsibilities

• Lead and contribute to the design, development, and deployment of GenAI applications using Kubernetes (GKE) and modern cloud-native patterns.
• Build and maintain CI/CD pipelines using tools like Argo CD, GitHub Actions, and infrastructure-as-code practices.
• Collaborate with ML engineers, infra teams, and product stakeholders to deliver secure, scalable, and observable services.
• Write clean, maintainable, and efficient code primarily in Python and other backend languages.
• Support front-end development and integration efforts where needed to ensure smooth end-to-end delivery.
• Provide technical leadership to a smaller group or squad through code reviews, mentorship, and architecture guidance.
• Monitor and optimize application health and performance using tools like Splunk and Datadog.
  

Requirements

What We're Looking For:

• Strong proficiency in cloud-native architecture and scaling solutions using GCP and GKE.
• Experience with CI/CD pipelines, ideally with Argo CD, GitHub Actions, and containerized deployments (Docker, Kubernetes).
• Proficient in Python and familiar with backend frameworks and patterns.
• Familiarity with observability and monitoring tools like Splunk, Datadog, or equivalents.
• Exposure to full-stack development (e.g., React, TypeScript) is a plus.
• Strong analytical, debugging, and problem-solving skills.
• Excellent communication and collaboration abilities; ability to work across cross-functional teams.
  

Bonus Points:

• Experience working with LLMs or Generative AI frameworks (e.g., LangChain, LlamaIndex).
• Prior contributions to AI infrastructure, MLOps, or large-scale microservices.
• Proven ability to lead or mentor junior developers and influence engineering practices.