At gFiber, we believe that great internet has the power to drive innovation, strengthen communities, enable the impossible, and do all the everyday things that make all of our world go round. And the job of creating better internet is never done - so we’re growing! Our team is committed to building a place where people who want to make a difference can grow their careers and find their spot to belong.
The application window will be open until at least November 2nd, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.
This role is not eligible for immigration sponsorship.
Role Description
gFiber is currently at a transformative crossroads. As we ramp up investments in AI-driven product features, the Platform Engineering team is the engine room for this evolution. We are modernizing our foundational systems to support a new generation of AI initiatives, focusing on:
- Agentic Platforms: Building the frameworks for autonomous AI agents.
- AI Observability & Security: Implementing guardrails and monitoring specifically for LLMs.
- AI-Centric DevOps: Developing specialized CI/CD pipelines and automated testing frameworks tailored for AI Agents and machine learning workflows.
We are seeking a Senior AI Platform Engineer to lead the charge in designing and deploying our next-generation agentic systems and LLM-powered platforms. In this high-impact role, you will bridge the gap between cutting-edge AI Architecture and scalable Cloud Infrastructure. You’ll be architecting the entire ecosystem—utilizing advanced prompt engineering, LLM stack and DevOps practices to build AI Platform and optimize gFiber’s enterprise workflows. You will be a champion for engineering excellence, driving automation strategies and sophisticated infrastructure standards across our entire product suite.
In this role, you'll:
- Architect and build the Internal AI Developer Platform (IDP), abstracting complex GCP AI services (Vertex AI, Agent Engine, Model Garden) into self-service, "paved-path" APIs, SDKs, or Terraform modules that product engineering teams can easily consume without needing deep AI infrastructure expertise
- Design and Build enterprise Model Gateways. Must have experience building unified routing layers that manage rate-limiting, load balancing, failovers, and unified telemetry, allowing the platform team to swap underlying models seamlessly without breaking downstream product applications.
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines grounded in internal client policies and technical documentation.
