AI-enabled Site Reliability Engineering

Job Description -

We are seeking an AI Site Reliability Engineer to join a forward-thinking engineering team that builds intelligent observability, monitoring, deployment automation, and production support solutions using AI-augmented development practices. This role includes hands-on production support for Pega Infinity applications, where you will apply engineering rigor to improve platform stability, accelerate incident response, and strengthen operational resilience. You will build software solutions for operational challenges, leveraging Gen AI to automate triage, streamline root cause analysis, and drive systems toward five 9s (99.999%) availability while ensuring capacity planning, redundancy, and the highest standards of reliability, security, and scalability. This is a hands-on engineering role where you will actively contribute to architecture, code, AI-driven tooling, and the continuous improvement of Pega production operations. Ideal for engineers who are curious, adaptable, and excited about working at the intersection of software engineering, AI, and mission-critical platform support.

High Availability & Resilience

Design and implement architectures that achieve and sustain 99.999% uptime across critical systems and Pega Infinity application environments
Define, measure, and track SLOs, SLIs, and error budgets
Build AI-powered self-healing systems with automated failover, redundancy, and graceful degradation
Perform AI-assisted capacity planning, demand forecasting, and load testing
Conduct chaos engineering practices enhanced with AI-driven failure prediction
Production Support for Pega Infinity Applications

Provide engineering-led production support for Pega Infinity applications, ensuring platform stability, incident resolution, and operational continuity
Monitor application health, queues, integrations, and batch processes to proactively identify and resolve issues before business impact occurs
Lead incident triage, troubleshooting, and service restoration for Pega production issues, partnering across infrastructure, middleware, database, and development teams as needed
Use Gen AI to accelerate incident investigation, summarize signal patterns, and recommend next-best actions for recurring operational problems
Drive permanent fixes through post-incident analysis, defect prevention, runbook improvements, and automation of repetitive support tasks
Observability & Monitoring

Design, build, and maintain AI-enhanced observability platforms covering metrics, logs, traces, and intelligent alerting
Implement AI-powered anomaly detection, predictive alerting, and proactive system health management for shared services and Pega application workloads
Leverage Gen AI to auto-generate and refine dashboards, alert rules, and runbooks
Build real-time availability dashboards with AI-driven trend analysis tracking 99.999% targets
Root Cause Analysis

Build AI-accelerated root cause analysis with thorough postmortems and actionable remediation
Build AI-powered diagnostic tools that automatically correlate logs, metrics, traces, and Pega operational signals
Use Gen AI to analyze incident patterns, predict recurring failures, and recommend preventive actions
Build AI agents that automate initial triage and preliminary RCA for common incident types
Continuously reduce MTTD and MTTR through AI-assisted workflows
Deployment Automation

Design and maintain AI-enhanced CI/CD pipelines with AI-driven deployment risk scoring
Implement progressive delivery with canary releases, blue-green deployments, and automated rollbacks triggered by AI anomaly detection
Leverage Gen AI to generate deployment scripts, IaC templates, and pipeline configurations
Support production releases, hotfixes, and change implementations for Pega Infinity applications with strong operational controls and risk awareness
Eliminate manual toil by building AI agents for repetitive operational tasks
AI-Driven Development
Use GenAI tools (GitHub Copilot, Cursor, ChatGPT) for coding, debugging, documentation, and operations
Developer ownership is non-negotiable. All code, whether human or AI-generated, must be reviewed, tested, and understood before merging
Design and develop AI agents for incident response, log analysis, capacity management, and operational automation
Define agent goals, tool use, memory, and orchestration logic for multi-step SRE workflows
Apply spec-driven development and continuously evaluate emerging Gen AI tools
Testing & Quality

Leverage Gen AI to generate tests, identify coverage gaps, and create edge-case scenarios
Drive AI-powered security scanning, performance testing, and reliability validation early in development
Modernization & Collaboration

Modernize existing monitoring, alerting, deployment, and support processes using AI-assisted workflows
Identify technical debt and propose AI-accelerated remediation strategies with measurable outcomes
Participate in architecture discussions, design reviews, code reviews, and production readiness activities
Develop and maintain prompt engineering guidelines and custom instructions for consistent AI-assisted development
Share learnings, patterns, and tooling insights with the broader team

8+ years of software engineering experience with a strong focus on SRE, DevOps, platform engineering, or enterprise application production support
Bachelor's degree in Computer Science or equivalent
Hands-on technically with ability to architect, review code, and contribute to critical decisions
Proven experience operating systems at 99.99% or higher availability
Hands-on experience supporting and troubleshooting enterprise production applications, including incident triage, root cause analysis, and service restoration
Hands-on experience with Pega Infinity applications in production support, platform operations, or engineering roles
Strong hands-on knowledge of Pega operational constructs and troubleshooting, including queues, agents, node types, DDS, Kafka, integrations/APIs, and database troubleshooting
Hands-on experience with GenAI coding tools and demonstrated ability to apply them effectively
Proficiency in .NET, with scripting skills in Python, Bash, or PowerShell
Experience building AI agents for operational automation
Strong prompt engineering skills with ability to contribute to team-level AI practices
Deep understanding of HA patterns including active-active, multi-region failover, and distributed consensus
Hands-on experience with observability platforms (Datadog, Splunk, Grafana, or similar)
Strong experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions or similar)
Proven track record leading RCA efforts and driving reliability improvements
Proven experience diagnosing production issues end-to-end, identifying root cause, and partnering with development teams on corrective and preventative improvements
Strong CS fundamentals including system design, networking, concurrency, and algorithms

Experience with Oracle, Mongo, SQL DBs
Experience with cloud platforms (PCF, GCP, AWS, or Azure)
Experience with containerization and orchestration (Docker, Kubernetes)
Knowledge of chaos engineering and ML-powered anomaly detection
Experience with legacy system modernization
Pega Infinity platform knowledge, including application support, deployment, and troubleshooting concepts
Experience with Pega platform components and integrations, such as queues, agents, node classification/types, DDS, Kafka, and API-based integrations
Experience troubleshooting database performance, query issues, and data-related failures in enterprise application environments
Experience supporting workflow-driven enterprise applications in regulated or high-availability environments