Senior Software Engineer - Infrastructure Reliability

At JFrog, we’re reinventing DevOps to help the world’s greatest companies innovate -- and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit and just all-around great people. Here, if you’re willing to do more, your career can take off. And since software plays a central role in everyone’s lives, you’ll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production -- a concept we call “liquid software.” Wouldnt it be amazing if you could join us in our journey?

Location: Bangalore (Hybrid)

Position Overview

We are seeking a Senior Software Engineer to join our Security Product team, focused on improving the reliability and resilience of our platform across customer environments. You will be embedded within the engineering team, investigating system outages and failures, identifying recurring patterns, and driving fixes - either independently or in collaboration with service owners. You will work closely with production engineering and SRE teams to build playbooks, conduct post-incident reviews, and ensure problems are properly addressed at their root cause.

Key Responsibilities

• Investigate system outages and production failures across customer environments (SaaS and self-hosted), spanning RabbitMQ, Kubernetes, Docker, Postgres, and cloud infrastructure (AWS, Azure, GCP).
• Identify recurring failure patterns and systemic weaknesses from incident data, and drive them to resolution - whether by writing Go code yourself (resilience features, infrastructure fixes, observability) or by collaborating with service owners to prioritize and address reliability gaps.
• Lead and participate in post-incident reviews - document root causes, corrective actions, and follow through to ensure issues are properly resolved.
• Collaborate with production engineering and SRE teams to develop and maintain operational playbooks and runbooks that reduce time-to-resolution.
• Diagnose root causes across the full stack - message queue failures, container lifecycle issues, cloud networking, disk and memory pressure, and deployment topology mismatches.
• Design and implement data migrations and lifecycle management for infrastructure components such as queue management and vhost operations.
• Emit and monitor operational metrics to proactively detect infrastructure degradation and measure service reliability.
  
  

• 7+ years of experience in software engineering, with at least 3+ years focused on debugging and solving infrastructure-level problems in distributed systems.
• Strong proficiency in Go; familiarity with Python and Helm is a plus.
• Deep hands-on experience with RabbitMQ or similar message brokers (Kafka, ActiveMQ) - including queue management, clustering, monitoring, and production troubleshooting.
• Solid working knowledge of Kubernetes (pod lifecycle, resource management, networking, debugging CrashLoopBackOff / OOMKilled scenarios) and Docker.
• Experience investigating production incidents and conducting post-incident reviews with clear root cause analysis and follow-through.
• Strong understanding of Linux systems, networking fundamentals, and cloud infrastructure (AWS, Azure, or GCP).
• Ability to read and interpret logs, thread dumps, heap dumps, and system metrics to isolate root causes under time pressure.
• Excellent analytical and problem-solving skills with a methodical approach to debugging.
• Strong written and verbal communication skills - ability to produce clear incident reports, root cause analyses, and playbooks, and to communicate effectively across engineering, SRE, and customer-facing teams.
  
  

• Experience with artifact management or software supply chain tools (e.g., JFrog Artifactory, JFrog Xray).
• Experience with observability stacks (Prometheus, Grafana, ELK/OpenSearch, Coralogix).
• Experience with infrastructure-as-code tools (Terraform, Helm, Ansible).
• Prior experience in a customer-facing technical role (escalation engineering, support engineering, or field engineering).
• Familiarity with AI-assisted development tools - experience with skills, rules, hooks, and setting up Agents for developer workflows.