Senior Full Stack Software Engineer

TypeScript React Node.js GraphQL Event-Driven Architecture AWS Serverless AI-Assisted Engineering Overview We’re looking for a Senior Full Stack Software Engineer to build and evolve modern, cloud-native applications. You’ll own features end-to-end — from designing performant React/TypeScript user experiences to building event-sourced Node.js services and GraphQL APIs running serverless on AWS. You’ll partner closely with product and design, contribute to system architecture in an event-driven, service-oriented environment, and help strengthen engineering practices around CI/CD, automated testing, observability, and security-by-design. We use a modern stack and SDLC, this is an AI-native engineering role. Our software development lifecycle is built around agentic AI: coding agents plan, implement, test, and review alongside us across the full pipeline. We expect senior engineers to be fluent operators of these tools — directing agents with precise intent, engineering the context they work from, and rigorously verifying what they produce. The bar for correctness, security, and maintainability does not move because an agent wrote the code; if anything, it rises. You will spend less time typing boilerplate code and more time specifying, orchestrating, reviewing, and owning outcomes. Key Responsibilities Product & Platform Delivery Own and deliver end-to-end product features from discovery and design through production supportBuild high-quality, accessible (WCAG 2.2 AA), and performant user interfaces using React and TypeScriptDesign and implement backend services and APIs using Node.js and GraphQL, with clear contracts and versioning strategiesDevelop and operate cloud-native and serverless workloads on AWS, including Lambda, EventBridge, Aurora, SQS/SNS, and DynamoDBContribute to service-oriented, event-driven, and event-sourced architectures that scale reliably and evolve safely over timeCommit clean, maintainable, well-documented code and participate in thoughtful code reviews that raise the standard for the whole teamContribute to architecture decisions and lightweight design records (ADRs, RFCs) that keep intent and trade-offs discoverable — by humans and by agents Agentic AI Development & Orchestration Work agent-first by default. Use coding agents (e.g., Claude Code, GitHub Copilot) as the primary implementation surface for well-scoped work, escalating to hands-on codingPractice spec-driven development. Translate product intent into precise, codebase-grounded specifications, acceptance criteria, and task decompositions that an agent can executeEngineer the context, not just the prompt. Author and maintain the artifacts agents depend on: repository instruction files, coding standards, architectural conventions, domain glossaries, reusable prompt and skill libraries, and golden reference implementationsOrchestrate multi-agent workflows. Decompose larger initiatives into parallelizable agent tasks, run and supervise concurrent agent sessions, and integrate their output into coherent, reviewable changesExtend the agent toolchain. Collaborate with teammates to build and maintain MCP servers, tools, and integrations that give agents safe, scoped access to our repositories, ticketing, documentation, observability, and internal servicesBuild AI-enabled product features where they create real user value — integrating LLM APIs, retrieval pipelines, and agentic workflows with attention to latency, cost, failure modes, and graceful degradationEvaluate what you ship. For AI-powered features, define and maintain evals, regression suites, and quality benchmarks; monitor for hallucination, prompt injection, and driftCollaborative & Team-Centric. Share patterns, prompts, workflows, and hard-won failure modes; help colleagues move from AI assistance toward safe, well-governed autonomy Quality, Testing & Verification Create and maintain automated tests — unit, integration, contract, and end-to-end — as the primary safety net for both human- and agent-authored changeDesign tests that verify behavior and intent rather than restating implementation, so they remain meaningful when agents refactor freelyChampion the definition of done: tests, docs, telemetry, security review, and rollback plan — not just a green build Delivery, CI/CD & Operations Build and improve CI/CD pipelines and release processes that enable fast, safe, repeatable, and reversible deploymentsDefine infrastructure as code (AWS CDK, Terraform, or CloudFormation) and treat environments as reproducible artifactsInstrument services with structured logging, metrics and participate in on-call, incident response, and blameless postmortems Security, Privacy & Compliance Apply secure coding practices, least-privilege access, and secrets hygiene as part of everyday developmentProtect sensitive data by design — encryption in transit and at rest, data minimization, and appropriate retentionUnderstand and mitigate AI-specific risks such as prompt injection, insecure tool/agent permissions, sensitive data leakage into models, and over-permissioned automation Collaboration & Technical Leadership Collaborate closely with product, design, QA, and engineering peers in an Agile environmentMentor engineers on system design, code quality, and effective, safe AI-assisted workflowsInfluence roadmap and technical direction by translating between business outcomes and engineering trade-offs Qualifications 5+ years of professional experience building and shipping production web applicationsStrong experience with React and modern frontend development practices, TypeScript-firstStrong backend experience with Node.js and event-based service designHands-on experience deploying and operating services in AWS environmentsFamiliarity with event-driven and event-sourced system designDemonstrated experience with automated testing and CI/CD pipelinesProficiency with Git-based workflows and collaborative, review-driven developmentPractical, day-to-day experience using AI coding assistants or agents in real production work — and a clear point of view on where they help, where they fail, and how you verify their outputStrong problem-solving skills and the ability to work effectively across the full stackExcellent written and verbal communication and the ability to collaborate cross-functionallyOwnership mindset: you take responsibility for outcomes in production, not just for merged pull requests Preferred Qualifications Experience with GraphQL and contract-first API developmentFamiliarity with Infrastructure as Code (AWS CDK, Terraform, or CloudFormation)Experience with observability tooling and OpenTelemetry-based instrumentationDatabase experience with SQL and/or NoSQL systems, including data modeling for event-sourced systemsExperience building software in regulated or security-sensitive environmentsExperience orchestrating multiple coding agents, or building internal tooling, MCP servers, or platform capabilities that make agents effective for a whole teamExperience shipping LLM-powered product features, including retrieval, tool use, prompt/version management, and evaluationExperience defining evals, guardrails, or governance for AI systems (e.g., aligned to the NIST AI Risk Management Framework or OWASP Top 10 for LLM Applications)Experience leading large-scale automated refactoring, migration, or modernization effortsContributions to developer experience, internal platforms, or engineering enablement How We Work Product-Led Teams with Strategic Focus. Engineers are embedded within cross-functional product teams that work from intentionally prioritized backlogs aligned to business outcomes. We empower engineers to understand the "why" behind the work and actively contribute ideas that shape solutions and product directionA Culture of Collaboration. Collaboration is central to how we work. Architecture reviews, design discussions, pair programming, mob sessions, and open RFCs are part of our normal rhythm. We share ideas early and often because the strongest solutions emerge through collective thinking and diverse perspectivesHumans own the outcome. Agents draft, propose, and automate. Engineers own architecture, security, business logic, and the decision to shipSmall batches, fast feedback, reversible decisions. We optimize for lead time and change-failure rate, not lines of code What Success Looks Like First 30 days: productive in the codebase and our agentic workflow; shipping small, reviewed changes to production with confidenceFirst 90 days: independently owning features end-to-end; improving at least one shared asset — a test suite, a pipeline stage, an agent context file, a quality gate — that makes the whole team fasterFirst year: trusted owner of a meaningful part of the system; a visible force multiplier for how the team designs, builds, verifies, and operates software. This is a fully remote position. INDCORHP Life Line Screening is proud to be an equal opportunity employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age disability, protected veteran status, or other characteristics protected by law. Life Line Screening will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditional upon the successful completion of a background check and drug screen. Privacy Notice to California Applicants Powered by JazzHR wH1YeQW4KU