Site Reliability Engineer - JL5

Infosys; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Infosys Bengaluru seeks a highly capable SRE professional to design, implement, and operate the lifecycle of critical services. You will build scalable, distributed systems, manage capacity and performance across public and private clouds, and integrate GenAI and AIOps to automate incident detection and remediation.

Responsibilities include defining standards for architecture, deployment, monitoring, and incident response, while driving automation and postmortems to improve efficiency.

Qualifications

• Experience with designing, building and operating large-scale distributed systems.
• Proficiency in Python, Ruby or Go and strong OO programming fundamentals.
• Hands-on experience with GenAI and AIOps platforms for automated incident detection and remediation.

Responsibilities

• Design and implement lifecycle of services from conception to deployment.
• Develop software solutions for operability of massive distributed systems handling millions of transactions and petabytes of data.
• Manage capacity and performance for scaling infrastructure across global clouds.
• Define and implement standards for system architecture, deployment, metrics, and operations.
• Support services through monitoring, availability, health checks and incident response.
• Improve performance and delivery through automation, postmortems, and configuration analysis.
• Engage in cross-group communications to align on observability and reliability goals.
• Troubleshoot and monitor production systems to ensure high uptime and rapid recovery.
• Enhance architectures with GenAI and AIOps tools for automated detection and faster resolution.
• Apply prompt engineering to improve AI-based observability and automation precision.
• Leverage AI capabilities across AWS Bedrock, Azure OpenAI and GCP Vertex AI to build intelligent SRE solutions.
• Develop ML models using operational telemetry for predictive analytics and automated actions.
• Evaluate and deploy AIOps platforms to improve observability and reduce noise.

Skills

Python

Go

Ruby

OO programming

Tools

AWS Bedrock

Azure OpenAI

GCP Vertex AI

Moogsoft

Dynatrace

Splunk

BigPanda

Datadog

Elastic

Job description

• Design and implement the lifecycle of services from conception to inception, including system design, build, and deployment
• Develop software solutions to enable operability of large-scale distributed systems capable of handling millions of transactions and petabytes of data
• Manage capacity and performance to help scale the infrastructure both on public and private clouds around the world
• Define and implement standards and best practices related to: System Architecture, Deployment, metrics, operational tasks
• Support services through activities such as monitoring availability, system health, and incident response
• Improve system performance, application delivery and efficiency through automation, process refinement, postmortem reviews, and in-depth configuration analysis
• Engage in Communications across all areas of the organization
• Troubleshooting and monitoring production systems to ensure the highest uptimes are maintained
• Support and improve upon existing high-availability architecture solutions as well as manage the operational activity.
• Integrate Generative AI (GenAI) and AIOps tools to automate incident detection, root cause analysis, and resolution workflows (e.g., self-healing scripts, intelligent runbooks), reducing manual toil and accelerating response times.
• Apply Prompt Engineering techniques to enhance interactions with AI-based observability and automation platforms improving accuracy and efficiency of AI responses.
• Leverage platform-specific AI capabilities (e.g., AWS Bedrock, Azure OpenAI, GCP Vertex AI) to architect intelligent SRE solutions tailored to cloud environments.
• Design, implement, and maintain AI/ML driven monitoring and alerting systems to proactively detect anomalies and predict potential failures, enabling preemptive remediation.
• Develop and train machine learning models using operational telemetry (logs, metrics, events, traces) to support predictive analytics and intelligent automation.
• Evaluate and deploy AIOps platforms (e.g., Moogsoft, Dynatrace, Splunk, BigPanda, Datadog, Elastic) to enhance observability, reduce noise, and accelerate incident resolution.
• Experience in one or more high level programming languages like Python or Ruby or GoLang and familiar with Object Oriented Programming.