Infrastructure / DevOps / Cloud Engineer 3-8+ years of Experience

Job Description — Infrastructure / DevOps / Cloud EngineerLocation

Onsite

Experience

3–8+ Years

Employment Type

Full-Time

About the Role

We are looking for a highly motivated Infrastructure / DevOps / Cloud Engineer to help build and maintain scalable infrastructure for our AI-driven chip design platform. This role focuses on cloud infrastructure, internal engineering tooling, CI/CD workflows, deployment automation, ML-Ops systems, and developer productivity infrastructure.

The ideal candidate should have strong Linux systems expertise and experience supporting modern AI/ML infrastructure and distributed workloads. You will work closely with AI engineers, backend engineers, and platform teams to enable reliable, scalable, and high-performance development and deployment environments.

This is a high-ownership role suited for engineers who thrive in fast-paced startup environments and enjoy solving complex infrastructure and scaling challenges.

Key Responsibilities

• Design, deploy, and maintain scalable cloud infrastructure
• Build and optimize CI/CD pipelines and deployment workflows
• Manage containerized environments using Docker and Kubernetes
• Automate infrastructure provisioning using Terraform and Infrastructure-as-Code practices
• Support AI/ML infrastructure including GPU workloads and distributed compute systems
• Improve developer productivity through internal tooling and automation
• Monitor infrastructure reliability, observability, and performance
• Manage scalable data and messaging infrastructure
• Support ML-Ops workflows, model deployment systems, and inference infrastructure
• Collaborate across engineering teams to improve platform scalability and reliability
• Troubleshoot production infrastructure and deployment issues rapidly

Required Skills

• Strong Linux systems administration and debugging skills
• Experience with Docker and Kubernetes
• Experience with CI/CD systems such as GitHub Actions
• Strong scripting skills in Python and/or Bash
• Experience with cloud platforms:
• AWS
• GCP
• Azure
• Infrastructure-as-Code experience using Terraform
• Experience with distributed systems and scalable backend infrastructure
• Familiarity with monitoring and observability systems

Preferred / Nice-to-Have Skills

Experience with one or more of the following is highly desirable:

• GPU infrastructure and AI inference systems
• ML-Ops platforms and workflows
• Ray
• Redis
• PostgreSQL
• Prometheus
• Grafana
• MLflow
• Kafka
• Celery
• Airflow
• Vector databases
• Distributed training or inference systems

What We’re Looking For

• Strong ownership mindset
• Ability to work autonomously with minimal supervision
• Startup mindset with strong execution ability
• Fast problem-solving skills
• Comfort working in high-growth, fast-paced engineering environments
• Strong communication and collaboration skills

Tools & Environment

The engineering team uses modern AI-assisted development workflows and internal tooling, including:

• Claude Code
• Codex
• Internal engineering productivity tooling

Compensation can vary significantly based on:

• Kubernetes & cloud expertise
• AI/ML infrastructure experience
• GPU systems experience
• Scale of systems previously handled
• Startup execution ability

Pay: ₹696,882.71 - ₹910,769.11 per year

Work Location: In person