Senior Site Reliability Engineer (AI)

The Challenge

• Own production services end-to-end, including reliability, scalability, and operational excellence
• Participate in on-call rotation and lead incident response

Your Mission

Engage and partner with various Engineering, Operations, and Product teams to design, deliver, and maintain a highly available and performant application platform.

• Collaborate with different functional groups to identify gaps, prioritize, and resolve issues

• Defining, implementing, and maintaining SLIs and SLOs aligned with customer experience.

• Design and instrument SLIs such as latency, error rates, and availability across critical services

• Manage and enforce error budgets to balance system reliability with product feature velocity.

• Improving alert quality by reducing noise and focusing on actionable, high-signal alerts

• Embed with product teams to review architectures and catch reliability risks early

• Share your knowledge and experience with the Engineering organization

• Share your findings with technical leadership and senior management

• Build scripts in python/bash/java or ruby for operational automation and incident response

You Are

A hands-on engineer familiar with running production services and providing understanding and solutions to appropriately monitor and automate those services.

Your Experience Includes

• Bachelor's degree in computer science, Engineering, or related technical or business field

• 4+ yrs. of application development experience with Java or other equivalent language

• Experience with Spring environment .

• Experience in cloud-based infrastructure (Azure, AWS, GCP, etc.)

• Experience with the factors influencing performance of software applications at multiple layers (Database, network, CPU utilization, JVM tuning, memory analysis, thread management, query performance etc.)

• An understanding of the importance of centralizing logging, metrics dashboards, and alerting. Able to talk about some of the tools used for these tasks

• A good understanding of databases (ideally SQL/NoSQL)

• Hands-on experience with observability tools (Datadog, Prometheus, Grafana, etc.)

• Familiarity with CI/CD pipelines and infrastructure-as-code (Terraform, Helm, jenkins, gitlab)

• Build and operate AI-assisted incident response systems (root cause analysis, log summarization, anomaly triage)

• Develop or integrate LLM-based tools to reduce MTTR and improve alert quality

• Apply machine learning techniques for anomaly detection, capacity prediction, or failure pattern analysis

• Experience deploying AI systems in production (not just experimentation)

• Familiarity with vector databases, embeddings, or RAG architectures for operational intelligence

• Strong understanding of prompt engineering and evaluation of LLM outputs in reliability workflow

• Kubernetes and container orchestration (EKS/AKS/GKE)

• Experience with distributed systems at scale
• Familiarity with service meshes and microservices architecture

Nice to Have

• Experience with chaos engineering tools (Gremlin, Chaos Monkey)

• Background in product-facing services with high traffic scale

• Knowledge of incident management platforms (PagerDuty/DataDog alerts)