Senior Software Engineer - Reliability

Jobgether SRL; India; Remote; INR 4,000,000 - 8,000,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive healthcare

Flexible time off

Remote-first environment

AI-native work environment

Accelerated growth

Global collaboration

Meaningful impact

Inclusive workplace

Job summary

Jobgether SRL in India is seeking a Senior Software Engineer - Reliability to design and scale AI-powered reliability systems for autonomous production operations. You will evolve a live multi-agent SRE platform, improving diagnostic accuracy, expanding safe automation, and building evaluation frameworks for trust and safety.

Accountabilities include improving incident investigation, expanding autonomous remediation with approvals, and establishing measurable reliability outcomes across

Qualifications

• Hands-on experience in site reliability engineering or production operations.

Responsibilities

• Improve AI-driven incident investigation and diagnostic accuracy.
• Expand automated remediation across operational scenarios with controlled autonomy.
• Build evaluation frameworks, fault-injection benchmarks, and performance metrics.

Skills

Operational engineering

Automation impact

AI-native engineering

Agentic systems and safety

Reliability evaluation

Software engineering fundamentals

Platform engineering

Engineering judgment

Ownership and accountability

Communication and collaboration

Continuous improvement

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Software Engineer - Reliability based in India. We are looking for an experienced Senior Software Engineer to build and scale AI-powered reliability systems that make production operations increasingly autonomous. In this role, you will help evolve a live, multi-agent AI Site Reliability Engineering (SRE) platform that investigates incidents, performs remediation, and resolves operational issues across production environments. You will focus on improving diagnostic accuracy, expanding safe automation, and developing rigorous evaluation frameworks that ensure AI agents can be trusted. Working within an established engineering architecture, you will eliminate recurring operational toil rather than simply automate existing manual tasks. You will also design safeguards that enable AI agents to operate safely and reliably in production. This is an opportunity to shape the future of autonomous operations in a remote-first, AI-native engineering environment.

Accountabilities

• Improve AI-driven incident investigation: Enhance the accuracy and reliability of investigation agents by identifying root causes, analyzing recurring diagnostic failures, and implementing systematic improvements that increase engineering trust.
• Expand automated remediation: Develop and scale remediation capabilities across a broader range of operational scenarios, introducing progressive autonomy through dry runs, human approvals, and controlled autonomous execution.
• Build robust evaluation frameworks: Design and maintain evaluation harnesses, fault-injection benchmarks, and performance metrics to measure agent reliability. Establish clear acceptance thresholds, report results using meaningful denominators, and validate the evaluation process itself.
• Implement production safety mechanisms: Develop fail-closed controls, kill switches, approval workflows, rollback strategies, and blast-radius limitations to minimize the impact of incorrect AI decisions in production environments.
• Develop AI agents to eliminate operational toil: Identify repetitive operational challenges and build targeted agents that remove entire categories of manual work, while creating reusable capabilities that simplify future agent development.
• Build a reusable reliability platform: Develop clean interfaces, documented failure modes, guardrails, and safe defaults that allow other engineering teams to adopt autonomous operational capabilities confidently.
• Maintain and improve existing systems: Contribute effectively to an established, well-documented codebase, following architectural decisions, engineering standards, and evaluation-gated development workflows.
• Respond to critical production issues: Take immediate action to stabilize affected systems when incidents occur, then investigate underlying causes and implement lasting solutions that prevent similar failures.
• Promote deterministic and safe engineering: Use conventional code for routing, filtering, and safety-critical decisions before introducing generative AI, ensuring that automation remains controlled, predictable, and measurable.
• Drive measurable operational outcomes: Evaluate the success of engineering initiatives through adoption, reliability improvements, and demonstrable reductions in operational effort, using transparent metrics to guide continuous improvement.

Requirements

• Operational engineering experience: Hands-on experience in site reliability engineering, production operations, incident response, technical support, or escalation management, including direct responsibility for incidents and their outcomes.
• Proven automation impact: Demonstrated ability to eliminate recurring operational problems rather than simply make existing procedures faster. You should be able to explain the problem addressed, the underlying failure category, and the measurable impact achieved.
• AI-native engineering experience: Experience fundamentally redesigning workflows using AI and delivering AI-enabled systems that other people rely on in their daily work.
• Agentic systems and safety: Experience building or architecting AI agents that can take actions autonomously is valuable. You should understand the risks of incorrect agent decisions and be comfortable designing appropriate guardrails, approval mechanisms, rollback paths, and blast-radius controls.
• Reliability evaluation and measurement: Strong analytical skills and an evidence-based approach to measuring system performance, including understanding failure rates, false positives, evaluation methodology, and the importance of statistically meaningful results.
• Software engineering fundamentals: Ability to write production-quality code, distinguish deterministic engineering requirements from generative AI use cases, and build maintainable solutions within an existing architecture.
• Platform engineering mindset: Ability to create reusable tools and interfaces for other teams, document failure modes, establish safe defaults, and encourage adoption through technical quality and practical value.
• Engineering judgment: Experience working within established codebases, respecting architectural standards, and improving existing systems without unnecessary rewrites.
• Ownership and accountability: A strong bias for action, comfort with ambiguity, and willingness to take responsibility for the consequences of systems operating in production.
• Communication and collaboration: Ability to explain technical decisions, share measurable outcomes, and collaborate effectively with engineering teams and stakeholders.
• Continuous improvement mindset: Genuine interest in reliability engineering, operational efficiency, AI-driven automation, and solving systemic problems rather than treating reliability work as a temporary career step.
• Working hours: Ability to maintain meaningful working-hour overlap, generally between approximately 11:00 a.m. and 8:00 p.m. India Standard Time.

Benefits

• Competitive compensation: Market-benchmarked base salary, performance-based variable pay, and impact-driven equity for most roles.
• Comprehensive healthcare: Health, dental, vision, and mental health coverage from day one, alongside flexible health stipends. Specific offerings depend on location.
• Flexible time off: Flexible paid time off and modern leave policies designed to support rest, personal commitments, and work-life balance.
• Remote-first flexibility: Work remotely within the applicable employment arrangements, with a high-trust culture that emphasizes ownership and asynchronous collaboration.
• AI-native work environment: Opportunities to build advanced AI systems and contribute to the development of autonomous operational technologies.
• Accelerated professional growth: Exposure to complex engineering challenges, modern technology, and experienced colleagues who value technical excellence and continuous learning.
• Global collaboration: Work with a diverse, internationally distributed team spanning more than 15 countries.
• Meaningful impact: Help shape production-grade AI reliability infrastructure and influence how engineering teams manage and resolve operational challenges at scale.
• Inclusive workplace: Join an environment committed to equal opportunity, diversity, and creating a workplace where employees can contribute authentically.