Senior Full Stack Software Engineer

Life Line Screening•Independence, OH

About The Position

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.

Requirements

5+ years of professional experience building and shipping production web applications.
Strong experience with React and modern frontend development practices, TypeScript-first.
Strong backend experience with Node.js and event-based service design.
Hands-on experience deploying and operating services in AWS environments.
Familiarity with event-driven and event-sourced system design.
Demonstrated experience with automated testing and CI/CD pipelines.
Proficiency with Git-based workflows and collaborative, review-driven development.
Practical, 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 output.
Strong problem-solving skills and the ability to work effectively across the full stack.
Excellent written and verbal communication and the ability to collaborate cross-functionally.
Ownership mindset: you take responsibility for outcomes in production, not just for merged pull requests.

Nice To Haves

Experience with GraphQL and contract-first API development.
Familiarity with Infrastructure as Code (AWS CDK, Terraform, or CloudFormation).
Experience with observability tooling and OpenTelemetry-based instrumentation.
Database experience with SQL and/or NoSQL systems, including data modeling for event-sourced systems.
Experience building software in regulated or security-sensitive environments.
Experience orchestrating multiple coding agents, or building internal tooling, MCP servers, or platform capabilities that make agents effective for a whole team.
Experience shipping LLM-powered product features, including retrieval, tool use, prompt/version management, and evaluation.
Experience 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 efforts.
Contributions to developer experience, internal platforms, or engineering enablement.

Responsibilities

Own and deliver end-to-end product features from discovery and design through production support.
Build high-quality, accessible (WCAG 2.2 AA), and performant user interfaces using React and TypeScript.
Design and implement backend services and APIs using Node.js and GraphQL, with clear contracts and versioning strategies.
Develop and operate cloud-native and serverless workloads on AWS, including Lambda, EventBridge, Aurora, SQS/SNS, and DynamoDB.
Contribute to service-oriented, event-driven, and event-sourced architectures that scale reliably and evolve safely over time.
Commit clean, maintainable, well-documented code and participate in thoughtful code reviews that raise the standard for the whole team.
Contribute to architecture decisions and lightweight design records (ADRs, RFCs) that keep intent and trade-offs discoverable — by humans and by agents.
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 coding.
Practice spec-driven development. Translate product intent into precise, codebase-grounded specifications, acceptance criteria, and task decompositions that an agent can execute.
Engineer 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 implementations.
Orchestrate multi-agent workflows. Decompose larger initiatives into parallelizable agent tasks, run and supervise concurrent agent sessions, and integrate their output into coherent, reviewable changes.
Extend 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 services.
Build 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 degradation.
Evaluate what you ship. For AI-powered features, define and maintain evals, regression suites, and quality benchmarks; monitor for hallucination, prompt injection, and drift.
Collaborative & Team-Centric. Share patterns, prompts, workflows, and hard-won failure modes; help colleagues move from AI assistance toward safe, well-governed autonomy.
Create and maintain automated tests — unit, integration, contract, and end-to-end — as the primary safety net for both human- and agent-authored change.
Design tests that verify behavior and intent rather than restating implementation, so they remain meaningful when agents refactor freely.
Champion the definition of done: tests, docs, telemetry, security review, and rollback plan — not just a green build.
Build and improve CI/CD pipelines and release processes that enable fast, safe, repeatable, and reversible deployments.
Define infrastructure as code (AWS CDK, Terraform, or CloudFormation) and treat environments as reproducible artifacts.
Instrument services with structured logging, metrics and participate in on-call, incident response, and blameless postmortems.
Apply secure coding practices, least-privilege access, and secrets hygiene as part of everyday development.
Protect sensitive data by design — encryption in transit and at rest, data minimization, and appropriate retention.
Understand and mitigate AI-specific risks such as prompt injection, insecure tool/agent permissions, sensitive data leakage into models, and over-permissioned automation.
Collaborate closely with product, design, QA, and engineering peers in an Agile environment.
Mentor engineers on system design, code quality, and effective, safe AI-assisted workflows.
Influence roadmap and technical direction by translating between business outcomes and engineering trade-offs.