Senior Platform Engineer - AI

The application window will be open until at least June 1, 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.
• 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 $137,200 - $221,900 + bonus + 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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