Senior Platform Engineer - AI

gFiber

15h•$137,200 - $221,900

About The Position

gFiber is seeking a Senior Platform Engineer to design and deploy next-generation agentic systems and LLM-powered platforms. The role bridges AI architecture and scalable cloud infrastructure, building an AI platform that supports product features and optimizes enterprise workflows. The Platform Engineering team is modernizing foundational systems for agentic platforms, AI observability and security, and AI-centric DevOps. The engineer will architect an Internal AI Developer Platform that abstracts GCP AI services into self-service APIs, SDKs, or Terraform modules; build enterprise model gateways; develop retrieval-augmented generation pipelines; and oversee deployment of microservices using GKE, Cloud SQL, and Cloud Build. The position also drives automation strategies and infrastructure standards across the product suite.

Requirements

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

Nice To Haves

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

Responsibilities

Architect and build an Internal AI Developer Platform that abstracts GCP AI services, including Vertex AI, Agent Engine, and Model Garden, into self-service APIs, SDKs, or Terraform modules.
Design and build enterprise model gateways that provide unified routing, rate limiting, load balancing, failovers, and telemetry, enabling underlying models to be changed without disrupting downstream applications.
Build and optimize retrieval-augmented generation (RAG) pipelines grounded in internal client policies and technical documentation.
Oversee deployment of microservices using GKE, Cloud SQL, and Cloud Build to ensure scalable and reliable AI performance.
Develop frameworks for autonomous AI agents and implement AI observability, security guardrails, and monitoring for LLMs.
Develop specialized CI/CD pipelines and automated testing frameworks for AI agents and machine learning workflows.
Drive automation strategies and infrastructure standards across the product suite.