AI Platform Engineer
Design, build, and maintain LLM integrations powering AI features. Own end-to-end delivery from requirements through production monitoring with focus on scalability and reliability.
About the job
What You’ll Do
- Develop and maintain LLM integrations to power AI features across solutions.
- Ensure scalability, reliability, and performance of AI features in production.
- Translate abstract requirements into structured, sound technical plans and milestones.
- Own implementations end-to-end: discovery/requirements → design → build → launch → post-delivery monitoring/iterating.
- Evaluate and articulate implications and trade-offs of technical choices.
- Leverage AI agents to improve development velocity and operational efficiency.
- Collaborate across engineering and adjacent teams to share learnings, improve processes, and continuously raise quality.
Requirements
- Strong proficiency in Python for production software.
- Proficiency with Jupyter Notebook or an equivalent environment (e.g., JupyterLab, Databricks, Colab, etc.).
- Demonstrated experience building, integrating, and operating LLM-powered features/services.
- Ability to decompose ambiguous problems, write clear technical plans, and execute with high ownership.
- Experience designing for reliability, scalability, and observability in production systems.
- You leverage AI Agents for day-to-day efficiency.
Nice to Have
- Terraform and Helm Charts for infrastructure and deployment.
- Google Cloud Platform (e.g., GKE, Cloud Run, Cloud Storage).
- Typescript for service or UI integrations.
- Postgres for application data modeling and performance.
- Experience with ML/AI platforms, agents, or orchestration frameworks.
Skills
Python, Llm Integration, Jupyter Notebook, Databricks, Terraform, Helm, GCP, GKE, Cloud Run, TypeScript, Postgres
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