Principal Service Reliability Engineer

Key Responsibilities

• End-to-end service ownership: design for telemetry, security, resiliency, scalability, and performance; lead sizing/architecture; drive service health reviews and process simplification.

• Incident management and prevention: lead postmortems/RCAs, coordinate fixes, define repair items, and implement data-driven prevention and continuous improvement.

• AI/ML and GenAI delivery: design and integrate solutions with LLMs, RAG, agentic workflows, and conversational AI; build low-latency model serving and retraining pipelines.

• Application engineering: develop performant microservices for distributed, containerized, cloud-native systems.

• Automation: eliminate toil by automating operational workflows, recovery procedures, code delivery, and configuration management; build internal tools and reusable scripts/services to accelerate delivery and reduce errors.

• Observability: define and implement monitoring, logging, alerting, and tracing strategies; establish SLOs/SLIs/error budgets; improve diagnostics and performance visibility for rapid triage.

• Cross-functional collaboration: partner with product, operations, and data teams to translate requirements into secure, scalable solutions; communicate effectively with technical and non-technical stakeholders.
  

Minimum Qualifications

• BS/MS in Computer Science or related field; 10+ years of software engineering in cloud environments.

• Strong in distributed systems/microservices using java / python; SQL/data modeling; python for AI/automation.

• SRE/DevOps expertise: systems and networking fundamentals, application security, observability, performance analysis, and incident response.

• Proven SDLC excellence: code quality, reviews, version control, CI/CD, testing, and release engineering.

• Excellent written and verbal communication; English fluency.
  

Preferred/Technical Skills

• AI/ML/GenAI: experience with foundational models, RAG, agentic architectures; model deployment, optimization, monitoring, and retraining.

• Cloud and containers: experience with containerization, orchestration, and resilient, fault-tolerant microservices.

• Observability: hands-on experience designing dashboards, alerts, traces, logs, and metrics; defining SLOs/SLIs and error budgets; on-call readiness and runbook quality.

• Operations: performance tuning across java / python and SQL for large-scale enterprise applications; strong Linux/Unix expertise; capacity planning and reliability reviews.

• Automation and scripting: proficiency in scripting to automate operational workflows, build tooling, and CI/CD tasks (e.g., shell scripting, python, configuration-as-code, task runners).

• Familiarity with enterprise ERP applications and standard DevOps tooling and practices.