DevOps / Cloud Infrastructure Engineer (GCP)

Company: Giniiris (https://giniiris.ai/)

Experience: 4 - 5 Years

Location: Gurugram

Type: Full-time

About Gini Iris:

Gini Iris is building the world's first AI-native Command Center for autonomous enterprise operations. Moving beyond generic LLM wrappers, we deploy global Virtual Operations Teams that seamlessly handle Omni-Channel CX delivery (Voice, Chat, Email, Social) with sub-200ms latency, deterministic logic guardrails, and enterprise-grade security.

To support real-time audio streaming, dynamic LLM routing, and continuous model inference, our underlying cloud infrastructure must be resilient, automated, and secure. As a DevOps Engineer, you will be a hands-on builder maintaining the high availability, security, and deployment pipelines of our Google Cloud Platform ecosystem.

The Role & Impact:

We are looking for an execution-focused DevOps / SRE Engineer with 4 to 5 years of hands-on experience in cloud-native environments. You will bridge development and operations by automating repetitive tasks, standardizing CI/CD pipelines, provisioning infrastructure via code, and managing our containerized workloads.

You will work closely with our Platform Head and development leads to ensure that deploying microservices, managing vector databases, and updating production environments is fast, frictionless, and secure.

Core Responsibilities:

- Infrastructure as Code (IaC): Write, maintain, and audit declarative infrastructure configurations using Terraform to provision and manage our GCP resources reliably.

- Kubernetes & Container Operations: Manage, monitor, and scale production clusters running on Google Kubernetes Engine (GKE) and serverless workloads on Google Cloud Run. Ensure optimal resource utilization and node pool sizing.

- CI/CD Automation: Design, build, and optimize automated build/test/deploy pipelines using GitHub Actions or GitLab CI, enabling safe blue/green or canary rollouts with zero downtime.

- Security & Network Configuration: Implement and enforce cloud security baselines: VPC networks, subnetting, firewall rules, Cloud Armor, IAM least-privilege policies, and automated secret rotations using Google Secret Manager.

- Observability & Alerting: Configure real-time telemetry, log aggregation, and dashboard alerts using Google Cloud Monitoring/Logging (formerly Stackdriver), Prometheus, Grafana, or Datadog to meet strict latency and uptime SLAs.

- Database & Storage Maintenance: Assist in managing backups, access control, and maintenance for production datastores including Firebase/Firestore, Cloud SQL, and Cloud Storage.

Required Technical Skills:

Core GCP & Cloud Architecture:

- 3+ years of dedicated, hands-on production experience with Google Cloud Platform (GCP).

- Deep proficiency with Google Kubernetes Engine (GKE): deployments, ingress controllers, config maps, secrets, and pod auto-scaling (HPA/VPA).

- Strong working knowledge of GCP networking: VPCs, Private Google Access, Cloud NAT, Cloud Load Balancing, and DNS.

Infrastructure as Code & Tooling:

- Strong proficiency with Terraform for state management, modular configuration, and environment separation (dev/staging/prod).

- Solid containerization skills with Docker (multi-stage builds, image size optimization, vulnerability scanning).

- Strong scripting skills in Bash and Python for cloud automation, cleanup jobs, and pipeline hooks.

CI/CD & Security Practices:

- Hands-on experience setting up continuous integration and delivery pipelines from scratch using GitHub Actions or similar platforms.

- Experience with automated vulnerability scanning (e.g., Trivy, Snyk) and adhering to SOC2/ISO compliance requirements.

Behavioral Traits & Mindset:

- Automation-First Reflex: If a task needs to be performed more than twice, your instinct is to automate it via a script or pipeline.

- High Operational Discipline: Careful with production access, double-checking blast radiuses before applying Terraform changes, and documenting runbooks clearly.

- Collaborative Partner: Able to assist developers in troubleshooting runtime errors, network timeouts, and container misconfigurations without friction.

Nice-to-Haves:

- Google Cloud Certification: Associate Cloud Engineer or Professional Cloud DevOps / Cloud Architect.

- Experience supporting machine learning or data science workloads on GCP (e.g., Vertex AI pipelines, GPU-backed node pools).

- Familiarity with Helm charts for managing Kubernetes application packages.