Azure DevOps Lead Technical Consultant

Job Summary

We are seeking a DevOps Engineer to build, secure, and maintain the cloud infrastructure supporting our Google Gemini Enterprise and Gemini Agents Platform. In this role, you will be responsible for automating CI/CD pipelines for custom GenAI applications, managing agent infrastructure, orchestrating vector databases, and ensuring high availability, low latency, and continuous observability across all deployed AI agent workflows.Key ResponsibilitiesInfrastructure as Code (IaC): Provision and maintain scalable, secure Google Cloud Platform (GCP) environments for Gemini Enterprise and Vertex AI using Terraform or Pulumi.CI/CD & Deployment Automation: Build automated deployment pipelines (GitHub Actions, Cloud Build, or GitLab) for LLM prompts, agent workflows, RAG pipelines, and API middleware.  LLM & Agent Operations (MLOps/LLMOps): Set up and manage lifecycle systems for prompt management, model evaluation (evals), fine-tuning pipelines, and vector database indexing (e.g., Vertex AI Vector Search, Pinecone, ChromaDB).Observability & Monitoring: Implement real-time monitoring and alerting for agent latency, token usage costs, API rate limits, model hallucination rates, and system uptime using Cloud Monitoring, Prometheus, Grafana, or OpenTelemetry.Security & Compliance (DevSecOps): Enforce zero-trust access policies (IAM), manage secret injection (Secret Manager), configure VPC service controls, and ensure data boundary protection for sensitive enterprise inputs.Scalability & Performance: Optimize API routing, load balancing, and containerized microservices (GKE / Cloud Run) to support high-throughput, low-latency AI agent operations.

Key Responsibilities

We are seeking a DevOps Engineer to build, secure, and maintain the cloud infrastructure supporting our Google Gemini Enterprise and Gemini Agents Platform. In this role, you will be responsible for automating CI/CD pipelines for custom GenAI applications, managing agent infrastructure, orchestrating vector databases, and ensuring high availability, low latency, and continuous observability across all deployed AI agent workflows.Key ResponsibilitiesInfrastructure as Code (IaC): Provision and maintain scalable, secure Google Cloud Platform (GCP) environments for Gemini Enterprise and Vertex AI using Terraform or Pulumi.CI/CD & Deployment Automation: Build automated deployment pipelines (GitHub Actions, Cloud Build, or GitLab) for LLM prompts, agent workflows, RAG pipelines, and API middleware.  LLM & Agent Operations (MLOps/LLMOps): Set up and manage lifecycle systems for prompt management, model evaluation (evals), fine-tuning pipelines, and vector database indexing (e.g., Vertex AI Vector Search, Pinecone, ChromaDB).Observability & Monitoring: Implement real-time monitoring and alerting for agent latency, token usage costs, API rate limits, model hallucination rates, and system uptime using Cloud Monitoring, Prometheus, Grafana, or OpenTelemetry.Security & Compliance (DevSecOps): Enforce zero-trust access policies (IAM), manage secret injection (Secret Manager), configure VPC service controls, and ensure data boundary protection for sensitive enterprise inputs.Scalability & Performance: Optimize API routing, load balancing, and containerized microservices (GKE / Cloud Run) to support high-throughput, low-latency AI agent operations.

Skill Requirements

GCP

IAM 

Other Requirements