Infrastructure Engineer (India)

Responsibilities

• Design, develop, test, deploy, maintain, and improve our cloud-based infrastructure, focusing on high availability, low latency, and cost-effectiveness.
• Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience.
• Be the subject matter expert in infrastructure when designing new products and introducing new technology to our existing product line.
• Develop automation scripts, tools, and frameworks to streamline deployment, monitoring, and maintenance processes.
• Implement robust security measures, adhering to industry standards and best practices, to safeguard sensitive data and prevent potential vulnerabilities.
• Analyze performance metrics, identifying areas for optimization and proposing data-driven improvements.
• Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
• Stay up-to-date with emerging trends, technologies, and methodologies, applying this knowledge to enhance our infrastructure capabilities.

Required Qualifications

• Professional experience: 7+ years of design, implementation, or consulting in applications and infrastructures experience.
• Education: BSc degree in Computer Science, Engineering, or a related field.

Preferred Qualifications

• Technology stack:
• Cloud Platforms: AWS, Azure, GCP
• Infrastructure as Code (IaC): Terraform, CloudFormation, Ansile, etc.
• Configuration Management: Ansible, Puppet, Chef
• Container Orchestration: Kubernetes, Docker Swarm
• Monitoring and Logging: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana).
• Programming/ Development: Python, Node.js, Bash, Go, Ruby, CI/CD pipelines, and version control.
• Education: Master’s or PhD in Computer science or related technical fields.

Professional Attributes (Code42)

• Practice Humility: You ask questions even when you think you know the answer. You seek feedback early, learn from anyone regardless of title, and treat every experiment — especially the failures — as data.
• Bias for Outcomes: You measure your work by what changed, not what you tried. You ship results, not slide decks. When a deadline is real, you find a way.
• Care Deeply: You treat every problem as yours to solve. You review your own work with the rigor you'd want from a reviewer. You help teammates without being asked.
• Dare to Do the Impossible & Embrace Scarcity: You set goals that make you uncomfortable. When told something can't be done, you find a way or a better question. Constraints sharpen your thinking, not slow it down.
• Build a Better World: You believe AI should make things meaningfully better for real people. You hold yourself accountable not just for whether your model works, but for what it does in the world.