Senior DevOps Engineer

Overview

Zime is building an AI-native behaviour and revenue intelligence. Our systems power real-time AI sales playbooks and workflows used by revenue teams at scale. Infrastructure reliability, scalability, and cost discipline are core to our success.

This role requires someone who is deeply hands-on, comfortable making high-impact infrastructure decisions, and capable of mentoring and guiding others as the company scales.

This is not a support role. This is a builder and owner role.

• Design, build, and own scalable, secure, and highly available cloud infrastructure
• Own production environments end-to-end, including uptime, performance, and cost optimization.
• Build and maintain CI/CD pipelines to enable fast and reliable releases.
• Familiarity with GCP and AWS.
• Implement and manage infrastructure as code (Terraform / Pulumi / CloudFormation)
• Lead incident response, root cause analysis, and post-mortems.
• Partner closely with engineering and ML teams to support AI workloads and data pipelines.
• Establish and maintain observability systems (monitoring, logging, alerting)
• Drive security, access control, and compliance best practices
• Mentor junior engineers while remaining hands-on when required
• Deploy on private cloud.

• Review and improve cloud architecture and deployment strategies.
• Debug and resolve real production issues
• Automate infrastructure and operational workflows
• Make practical trade-offs between speed, cost, and reliability
• Set DevOps standards and raise the overall engineering bar.

• 10 years+ of hands-on experience in DevOps, Platform Engineering, or SRE roles
• Tier-1 engineering pedigree (IITs / IISc / top global CS programs) OR
• Exceptional builders from non‑IIT backgrounds who demonstrate rare depth, speed, and judgment.
• Early startup engineers with deep ownership scars, including:
• Built zero → one systems
• Shipped under ambiguity
• Owned production outages and rewrites
• Made hard architectural tradeoffs without guardrails
• Strong experience with at least one major cloud provider (GCP and AWS)
• Strong fundamentals in Linux, networking, and distributed systems
• Production experience with Docker and Kubernetes
• Experience building and maintaining CI/CD pipelines
• Hands-on experience with infrastructure as code tools
• Proven experience owning and supporting production systems
• Ability to lead, mentor, and collaborate while staying deeply hands-on

• Experience supporting AI/ML workloads (model training, inference pipelines, GPU workloads)
• Startup or high-growth environment experience.
• Exposure to security, compliance, or SOC2 readiness
• Public GitHub repositories, blogs, or technical contributions.

• Stable, scalable, and cost-efficient production infrastructure
• Faster and more reliable engineering releases
• Reduced incidents and clear ownership when issues arise
• Strong collaboration between DevOps, engineering, and ML teams
• Clear technical leadership in infrastructure and reliability.

• Work closely with founders on core technical decisions
• Build infrastructure for a true AI-native product
• High ownership, high trust, and minimal bureaucracy.
• Opportunity to shape and lead the DevOps function as we scale