DevOps Engineer

About The Role

We are looking for a DevOps Engineer-I to help manage and scale our cloud infrastructure and

deployment systems. You will work closely with the engineering team to automate deployments,

improve system reliability, and manage Kubernetes-based infrastructure.

As part of our platform evolution, you will also support AI-driven systems and data pipelines,

helping deploy and operate services that power AI/ML features.

This role is ideal for someone who enjoys automation, cloud infrastructure, Kubernetes, and

modern AI-enabled platforms.

Key Responsibilities / What You'll Do:

Manage and maintain Kubernetes clusters for production and staging environments.

Deploy and manage applications using containerized infrastructure (Docker + Kubernetes).

Build and maintain CI/CD pipelines for automated and reliable deployments.

Manage cloud infrastructure (AWS/GCP) and ensure scalability, availability, and performance.

Support deployment and scaling of AI/ML services, APIs, and data pipelines.

Implement infrastructure to support AI workloads and model-serving systems.

Monitor system health using observability tools (logs, metrics, tracing).

Automate infrastructure provisioning using Infrastructure as Code.

Troubleshoot infrastructure, networking, and deployment issues.

Implement security best practices, secrets management, and access controls.

Experience & Qualifications:

13 years of experience in DevOps / Site Reliability / Cloud Engineering.

Hands-on experience with Kubernetes (pods, deployments, services, ingress, scaling).

Strong experience with Docker and containerized workloads.

Experience with AWS or GCP cloud platforms.

Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.).

Strong understanding of Linux systems and shell scripting.

Familiarity with networking, load balancing, and distributed systems.

Experience with monitoring and logging tools (Prometheus, Grafana, Datadog, CloudWatch,

etc.

Good to Have

Experience deploying AI/ML services or model-serving frameworks.

Familiarity with LLM infrastructure, vector databases, or AI APIs.

Experience with Terraform or other Infrastructure-as-Code tools.

Experience managing NGINX / ingress controllers.

Experience with Helm charts.

Exposure to data pipelines or ML workflows.

What We Offer

Opportunity to work on scalable cloud infrastructure and modern AI-powered systems.

Ownership of DevOps processes and infrastructure

A fast-paced startup environment with high learning opportunities.

Competitive compensation and growth opportunities.