Senior Software Engineer - Reliability
J
JobgetherAI reliability
Based in India, Meaningful working-hour overlap, generally between approximately 11:00 a.m. and 8:00 p.m. India Standard Time.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Product Operations
Requirements
- Have hands-on experience in site reliability engineering, production operations, incident response, technical support, or escalation management, with direct responsibility for incidents and outcomes.
- Demonstrate that you have eliminated recurring operational problems and can explain the problem, failure category, and measurable impact.
- Have experience redesigning workflows using AI and delivering AI-enabled systems that people rely on in their daily work.
- Bring experience building or architecting autonomous AI agents, or understand the risks of incorrect agent decisions.
- Be able to design guardrails, approval mechanisms, rollback paths, and blast-radius controls.
- Use an evidence-based approach to measurement, including failure rates, false positives, evaluation methodology, and statistically meaningful results.
- Write production-quality code and distinguish deterministic engineering requirements from generative AI use cases.
- Build maintainable solutions within an existing architecture and respect established engineering standards.
- Create reusable tools and interfaces, document failure modes, and establish safe defaults for other teams.
- Explain technical decisions and measurable outcomes, and collaborate effectively with engineering teams and stakeholders.
- Maintain meaningful working-hour overlap, generally between approximately 11:00 a.m. and 8:00 p.m. India Standard Time.
Responsibilities
- Improve incident investigation agents by identifying root causes and recurring diagnostic failures.
- Expand automated remediation using dry runs, human approvals, and controlled autonomous execution.
- Design evaluation harnesses, fault-injection benchmarks, performance metrics, and acceptance thresholds.
- Implement fail-closed controls, kill switches, approval workflows, rollback strategies, and blast-radius limitations.
- Build targeted AI agents that eliminate repetitive operational work and create reusable capabilities.
- Develop interfaces, documented failure modes, guardrails, and safe defaults for a reusable reliability platform.
- Maintain and improve the established codebase in line with architectural decisions and engineering standards.
- Stabilize systems during critical production incidents, investigate causes, and implement lasting solutions.
- Use conventional code for routing, filtering, and safety-critical decisions before introducing generative AI.
- Measure adoption, reliability improvements, and reductions in operational effort.
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