Sr. Backend Engineer at resilientco

<div> <h2>Summary</h2> <p>We are seeking a senior backend engineer to join the Bill Pay production support team, helping keep mission-critical payment services reliable and performant. This role is focused on rapid, autonomous incident resolution, reducing operational friction through automation, and maintaining high availability for payment systems.</p> <p>The position emphasizes practical, hands-on use of AI tools (LLMs, copilots, agents) as productivity accelerators for debugging, code generation, log review, and documentation, while remaining an engineering-first production support role rather than an ML research position. This is an opportunity to shape runbooks and automations that directly improve service reliability and operational efficiency. The ideal candidate should be able to demonstrate how AI has improved their real engineering workflow through concrete prompt patterns, coding/debugging setups, review loops, scripts, templates, or repeatable automations.</p> <h2>Responsibilities</h2> <ul> <li>Maintain and operate Bill Pay backend services to ensure high availability and performance.</li> <li>Diagnose and resolve production incidents autonomously and under pressure.</li> <li>Partner with customer support to investigate and remediate recurring and new issues.</li> <li>Design and implement automations (scripts, templates, workflows) to eliminate repetitive operational tasks.</li> <li>Use AI tools pragmatically to accelerate debugging, generate code snippets, review logs, and produce documentation.</li> <li>Convert AI experiments into repeatable, reliable workflows and runbooks.</li> <li>Create and maintain clear runbooks, postmortems, and documentation so knowledge is shareable.</li> <li>Communicate status, risks, and blockers proactively to stakeholders and support teams.</li> <li><strong>Identify repetitive or slow operational tasks and build small scripts, templates, automations, or AI-assisted workflows to reduce friction.</strong></li> <li><strong>Experiment with AI tools intentionally to solve real bottlenecks, not simply because they are new or trending.</strong></li> <li><strong>Validate AI-generated outputs before using them in production workflows, documentation, scripts, or incident analysis.</strong></li> </ul> <h2>Requirements</h2> <ul> <li>5+ years of backend engineering experience, with strong production support responsibilities.</li> <li>Proven experience with Java for backend services, APIs, and microservices.</li> <li>Proficiency in Python for scripting, automation, and tooling.</li> <li>Strong SQL skills and experience debugging and optimizing queries.</li> <li>Experience working with MySQL and PostgreSQL in production environments.</li> <li>Hands-on experience with Datadog for monitoring and observability.</li> <li>Experience with containerization and orchestration using Docker and Kubernetes.</li> <li>Practical experience with AWS for deploying and operating services.</li> <li>Demonstrable, practical use of AI tools (LLMs, copilots, agents) in day-to-day work, including examples of prompts, automations, or workflows created.</li> <li>Strong incident management, troubleshooting, and communication skills; able to proactively report status and risks.</li> <li><strong>Ability to describe how AI changed their workflow through prompt patterns, coding setups, review loops, automations, or templates.</strong></li> <li><strong>Good judgment on when not to use AI, and how to validate AI-generated outputs before relying on them.</strong></li> <li><strong>Experience turning one-off AI experiments into repeatable workflows, runbooks, scripts, or team practices.</strong></li> <li><strong>Ability to identify real workflow bottlenecks and use AI intentionally to reduce operational friction.</strong></li> </ul> <h2>Nice to Have</h2> <ul> <li>Experience with Terraform or Jenkins.</li> <li>Familiarity with MongoDB.</li> <li>Experience with Google Cloud Platform or Microsoft Azure.</li> <li>Practical experience using TypeScript in tooling or automation contexts.</li> </ul> </div> Selection Process: • Meeting Gime from Resilient Co. team. • Client technical interview with code challenge • Final Client Interview (2 round - 1hour each - cultural + technical interview)