Job Description:

Role : Senior Site Reliability Engineer (AI)

Location : Bengaluru, India | Engineering

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.

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

Nice to Have:

- 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:

Your Experience Includes:

- Bachelors 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.

- 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).