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Archwell Essentials

Full Stack AI Software Engineer

Full-Stack EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • Five or more years of professional software-engineering experience across the design, development, testing, deployment, and support of production applications.
  • At least two to three years of professional experience engineering software using modern artificial-intelligence-assisted engineering tools and artificial-intelligence development practices.
  • Demonstrated ability to build and support full-stack applications from concept through production.
  • Strong proficiency in at least one modern backend language, such as Python, Java, C#, Go, or TypeScript/Node.js.
  • Strong proficiency with modern frontend development using JavaScript or TypeScript and a framework such as React, Angular, or Vue.
  • Experience designing and consuming RESTful APIs, event-driven services, microservices, and enterprise integrations.
  • Experience with relational databases, SQL, data modeling, and at least one modern nonrelational or vector-database technology.
  • Practical experience integrating large language models or generative-artificial-intelligence services into production or enterprise applications.
  • Experience with artificial-intelligence coding tools such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, or comparable platforms.
  • Experience with Git-based source control, automated testing, code review, continuous integration, continuous delivery, and modern DevOps practices.
  • Experience deploying and operating applications in a major cloud environment such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
  • Working knowledge of containers, infrastructure as code, observability, application security, and vulnerability remediation.
  • Strong understanding of secure software-development principles, including authentication, authorization, encryption, secrets management, dependency management, and common application-security risks.
  • Demonstrated ability to work directly with business users, understand workflows, challenge assumptions constructively, and convert business problems into working software.
  • Ability to independently manage multiple priorities and maintain ownership of up to three applications.
  • Bachelor's or master's degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, or a related discipline, or equivalent practical experience.

Responsibilities

  • Partner directly with business stakeholders to understand operating processes, identify root problems, define measurable outcomes, and engineer effective technical solutions.
  • Translate ambiguous business needs into clear functional requirements, technical designs, user stories, acceptance criteria, and production-ready software.
  • Work interactively with users through rapid prototyping, demonstrations, feedback sessions, and iterative releases.
  • Build new applications and enhance existing platforms across the frontend, backend, application programming interface, integration, data, and infrastructure layers.
  • Develop intuitive, accessible, responsive, and high-performing user experiences.
  • Design reusable services, components, APIs, workflows, and integration patterns.
  • Make build-versus-buy and configuration-versus-custom-development recommendations.
  • Balance delivery speed with security, reliability, maintainability, scalability, and long-term architectural integrity.
  • Use modern artificial-intelligence engineering tools throughout the software development lifecycle to improve development speed, quality, testing, documentation, and maintainability.
  • Design and implement artificial-intelligence-enabled capabilities using large language models, retrieval-augmented generation, embeddings, vector search, structured outputs, tool calling, multimodal models, and agentic workflows.
  • Build artificial-intelligence agents that can securely interact with applications, APIs, databases, documents, and enterprise systems.
  • Develop prompts, system instructions, context-management strategies, tool definitions, workflows, and reusable artificial-intelligence skills.
  • Evaluate and select models based on accuracy, latency, security, cost, reliability, and business requirements.
  • Implement model evaluations, regression tests, guardrails, human-review controls, grounding, observability, and fallback mechanisms.
  • Identify and mitigate hallucination, prompt-injection, data-leakage, model-bias, unsafe-output, and unauthorized-tool-use risks.
  • Measure artificial-intelligence solutions using quality, accuracy, safety, performance, adoption, and business-value metrics.
  • Own software delivery from initial discovery and architecture through development, testing, deployment, production support, and continuous improvement.
  • Write clean, modular, testable, secure, and well-documented production code.
  • Develop and maintain unit, integration, application programming interface, user-interface, regression, performance, security, and end-to-end automated tests.
  • Use artificial-intelligence-assisted testing and automation tools while independently validating generated code and test results.
  • Participate in peer reviews, design reviews, architecture reviews, threat modeling, and release-readiness assessments.
  • Build and maintain continuous integration and continuous delivery pipelines.
  • Release software through safe, incremental, observable, and reversible deployment practices.
  • Ensure functional requirements and acceptance criteria are demonstrably satisfied before production release.
  • Serve as the accountable engineer or technical owner for up to three assigned applications.
  • Monitor application availability, performance, capacity, security, errors, and user experience.
  • Diagnose and resolve production incidents, defects, integration failures, and performance issues.
  • Perform maintenance, framework and dependency upgrades, operating-system or platform updates, certificate renewals, configuration changes, and technical-debt remediation.
  • Patch application and infrastructure vulnerabilities within established remediation timelines.
  • Maintain application documentation, support procedures, runbooks, architecture diagrams, data-flow diagrams, deployment instructions, and recovery procedures.
  • Participate in incident response, root-cause analysis, corrective-action planning, disaster-recovery testing, and business-continuity exercises.
  • Recommend modernization, re-platforming, consolidation, or retirement when an application no longer meets business or technical standards.
  • Implement secure coding standards, identity and access controls, encryption, secrets management, vulnerability management, logging, monitoring, and regulatory controls.
  • Ensure solutions align with approved technology standards, integration patterns, cloud strategies, and target-state architecture.
  • Collaborate on data models, storage patterns, data quality, lineage, retention, privacy, and performance.
  • Coordinate with support and operations teams to ensure applications can be monitored, supported, maintained, and recovered.
  • Follow software-development lifecycle, change-management, security, architecture, data-governance, and release-management standards.
  • Ensure applications comply with legal, regulatory, privacy, records-management, accessibility, and industry requirements.
  • Maintain traceability between business requirements, technical designs, code changes, tests, approvals, and releases.
  • Protect confidential, proprietary, customer, and regulated data throughout the development and production lifecycle.
  • Ensure artificial-intelligence-generated code and content are reviewed, tested, licensed appropriately, and treated with the same rigor as human-created work.
  • Contribute reusable engineering patterns, libraries, templates, prompts, skills, development standards, and lessons learned to the broader engineering organization.
  • Mentor other engineers and help raise the organization’s standards for artificial-intelligence-enabled software delivery.

Desired Qualifications

  • Experience building artificial-intelligence agents, retrieval-augmented generation solutions, semantic-search applications, document-processing systems, or workflow automation.
  • Experience with model-evaluation frameworks, prompt testing, artificial-intelligence observability, red-team testing, and responsible-artificial-intelligence controls.
  • Experience with frameworks or platforms such as LangGraph, Semantic Kernel, OpenAI Agents SDK, Model Context Protocol, or comparable orchestration technologies.
  • Experience with Kubernetes, serverless architectures, messaging platforms, API gateways, and distributed systems.
  • Experience working in a regulated industry such as financial services, mortgage lending, banking, insurance, healthcare, or government.
  • Understanding how mortgage products, servicing operations, borrower experience, compliance, and technology intersect to create business value.
  • Ability to communicate with mortgage business stakeholders without extensive business translation.
  • Ability to design technology solutions that improve operational efficiency while maintaining compliance and reducing risk.
  • Familiarity with privacy, model risk, information security, records retention, accessibility, and regulatory compliance requirements.
  • Experience modernizing legacy applications and integrating them with cloud, data, and artificial-intelligence platforms.
  • Experience developing software in close collaboration with product designers, business operators, and end users.
  • Demonstrated ability to improve development velocity without compromising engineering quality or operational stability.

About the Company

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