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Site Reliability Engineer

Job in Bedford, Hillsborough County, New Hampshire, 03110, USA
Listing for: Jobtailor
Full Time position
Listed on 2026-07-20
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 110000 - 160000 USD Yearly USD 110000.00 160000.00 YEAR
Job Description & How to Apply Below

Responsibilities

  • Build software solutions to improve reliability, reduce operational toil, and scale production systems—not just respond to issues.
  • Develop production-quality code using Node.js / JavaScript / Type Script and Python/Power Shell , including testing and documentation.
  • Leverage modern development tooling such as VS Code and AI-assisted tools (e.g., Git Hub Copilot) to accelerate delivery and problem-solving.
  • Independently own well-scoped features, fixes, or improvements end-to-end—from design through deployment and operational validation.
  • Participate in on-call rotations, respond to incidents, execute runbooks, and ensure clear communication and handoffs.
  • Analyze incidents and recurring issues to identify patterns, reduce alert noise, and implement durable fixes.
  • Implement and improve observability (logging, metrics, dashboards, alerts) for owned services.
  • Build automations, scripts, and lightweight tools to eliminate repetitive manual work and improve operational efficiency.
  • Identify and act on opportunities to improve system reliability, performance, and maintainability.
  • Develop an understanding of how systems impact customer experience and business outcomes.
Requirements
  • ~2 plus years of experience in SRE, software engineering, Dev Ops, or production engineering.
  • Strong hands-on coding skills with emphasis on:
    Node.js / JavaScript / Type Script
  • Python for scripting and automation
  • Experience building tools, APIs, or automations to solve engineering or operational problems.
  • Familiarity with AI-assisted development workflows (e.g., Git Hub Copilot, code generation tools) and interest in applying AI/LLMs to improve engineering productivity.
  • Foundational knowledge of:
    Monitoring, logging, and observability concepts
  • Distributed systems and API-based architectures
  • SQL and data analysis for troubleshooting
  • Exposure to cloud platforms (AWS or Azure), CI/CD pipelines, and modern development practices.
  • Basic understanding of incident management, problem management, and production support processes.
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