Associate, Platform Engineer

Enterprise AI’s platform team needs engineers who are learning the trade in a production AI setting. The Associate Platform Engineer builds the pipeline of platform engineering capability the team needs as its portfolio grows.

• Delivers defined platform tasks under direction from senior engineers, contributing to the health and quality of the platform estate.
• Builds AI-native fluency from the start. AI coding assistants are the default mode of work, not an occasional aid.
• Grows into full Platform Engineer responsibility with active support and mentorship from the Lead and Senior Platform Engineers.

Responsibilities:

• Maintain runbooks, FAQs, and onboarding materials for the platforms, contributing to institutional knowledge.
• Apply established platform standards and patterns; flag inconsistencies to senior engineers.

Learning & Development

• Build familiarity with the platform estate and developer tooling stack (GitHub Copilot, Claude Code/Cowork, Codex, Databricks AI Assistant, Accelerator platform) and core Google Cloud services (IAM, Cloud Storage, BigQuery, Cloud Run, Vertex AI).
• Learn GCP security basics (IAM, Secret Manager, data access controls) and grow toward CMEK, VPC Service Controls, and GxP awareness over time.
• Actively learn from senior engineers and build the skills to grow into Platform Engineer I responsibilities.

Education:

• Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.

Required skills :

About 2 years of experience or a relevant internship in software engineering, cloud platforms, or DevOps.

• Working proficiency in Python and SQL.
• Working knowledge of at least one cloud platform (GCP preferred; Azure or AWS acceptable).
• Hands-on use of Terraform or another IaC tool, and basic CI/CD pipeline experience.
• Daily use of AI coding agents (Claude Code, Codex, GitHub Copilot, Databricks AI Assistant) as a primary development mode.

Preferred skills:

• Familiarity with GitOps, container tooling (Docker, Cloud Run or GKE), or CI/CD pipelines.
• Coursework or projects involving AI/ML systems or LLM applications.
• Exposure to BigQuery, Vertex AI, or Databricks through a project, internship, or coursework.
• Foundational cloud certification (Google Cloud Digital Leader, Associate Cloud Engineer, Azure Fundamentals, or similar).