Senior Platform Engineer - AI

GFiber•Austin, TX

36d•$156,800 - $229,700

About The Position

GFiber is at a transformative crossroads, investing heavily in AI-driven product features. The Platform Engineering team is central to this evolution, modernizing foundational systems to support new AI initiatives. This includes building frameworks for autonomous AI agents, implementing guardrails and monitoring for LLMs, and developing specialized CI/CD pipelines and automated testing for AI Agents and machine learning workflows. GFiber is seeking a Senior AI Platform Engineer to lead the design and deployment of next-generation agentic systems and LLM-powered platforms. This role involves bridging AI architecture and scalable cloud infrastructure, architecting the ecosystem using prompt engineering, LLM stacks, and DevOps practices to build AI platforms and optimize enterprise workflows. The engineer will champion engineering excellence, driving automation and infrastructure standards across the product suite.

Requirements

Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
8 years of experience in setting up SDLC, CI/CD pipelines, automation, troubleshooting, launching and supporting enterprise applications as an individual contributor and in a Lead capacity.
5 years of experience as senior platform engineer, with recent years dedicated to architecting and scaling enterprise AI infrastructure.
Demonstrated expertise in building multi-agent systems, workflow automation, and implementing emerging integration frameworks (such as A2A and MCP).
5 years of hands-on experience with public cloud and Infrastructure as Code (IAC).
Experience with Python, Java and GCP infrastructure tools (GKE, CloudRun, CloudFunctions, GCS, etc).
Experience with cloud infrastructure management and automation technologies (Terraform, Ansible etc).

Nice To Haves

Experience optimizing applications, both stand-alone and in distributed systems to maximize performance.
Hands-on experience with Google Cloud Platform (GCP).
Experience with multi-cloud environments and other cloud providers (AWS, Azure, etc.).
Problem-solving and analytical skills.
Communication and teamwork skills.

Responsibilities

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, including 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.
Oversee the deployment of microservices using GKE (Google Kubernetes Engine), Cloud SQL, and Cloud Build, ensuring scalable and reliable AI performance.