Role Description
GFiber is at a transformative crossroads, ramping up investments in AI-driven product features. The Platform Engineering team is crucial for this evolution, modernizing foundational systems to support new AI initiatives. This includes building Agentic Platforms for autonomous AI agents, implementing AI Observability & Security for LLMs, and developing AI-Centric DevOps with specialized CI/CD pipelines and automated testing frameworks for AI Agents and machine learning workflows.
We are seeking a Senior AI Platform Engineer to lead the design and deployment of next-generation agentic systems and LLM-powered platforms. This high-impact role bridges cutting-edge AI Architecture and scalable Cloud Infrastructure, requiring you to architect the entire ecosystem using advanced prompt engineering, LLM stack, and DevOps practices to build an AI Platform and optimize GFiber’s enterprise workflows. You will champion engineering excellence, driving automation strategies and sophisticated infrastructure standards across the 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.