This role is not eligible for immigration sponsorship.Role DescriptionGFiber 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.
- MLOps & Deployment: Oversee the deployment of microservices using GKE (Google Kubernetes Engine), Cloud SQL, and Cloud Build, ensuring scalable and reliable AI performance.
At a minimum we'd like you to have:- 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) and experience with cloud infrastructure management and automation technologies (Terraform, Ansible etc)
It's preferred if you have:- 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
The US base salary range for this full-time position is between $156,800 - $229,700 + bonus + cash award + benefits. As pay varies by location, your recruiter will share more about the specific salary range for your targeted location during the hiring process.
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