Varmoda is hiring an experienced AI Full-Stack Engineer to design, develop, and deploy AI-driven workflow automation solutions. The ideal candidate will have expertise in full-stack development, generative AI, large language models, cloud-native technologies, and enterprise integrations, with a proven ability to deliver secure, scalable, and production-ready applications.
Key Responsibilities - Design, develop, test, deploy, and maintain an AI-enabled enterprise intake application and its supporting services.
- Build responsive, accessible, and user-friendly front-end components using modern JavaScript frameworks.
- Develop scalable back-end services, business logic, data-access components, and integration layers.
- Translate business requirements, user stories, and architecture specifications into maintainable production code.
- Develop reusable components, shared libraries, and common services that support future expansion.
- Apply software engineering standards covering code quality, maintainability, scalability, observability, and performance.
- Participate in peer reviews, technical design discussions, demonstrations, and release-readiness activities.
- Troubleshoot application defects, performance issues, integration failures, and environment-related problems.
- Build AI-guided workflows that help users capture business needs, objectives, success measures, constraints, and high-level requirements.
- Develop intelligent prompts, guided questions, recommendations, summaries, and next-step suggestions.
- Implement validation logic to improve the completeness, consistency, and quality of submitted information.
- Design user experiences that simplify complex intake and request-management processes.
- Connect AI-generated outputs to downstream workflow, review, and approval activities.
- Include appropriate human review for material recommendations and decisions.
- Capture user feedback and apply it to improve the accuracy and usability of AI-assisted experiences.
- Document prompt logic, workflow behavior, assumptions, limitations, and expected outcomes.
- Design and develop conversational AI experiences using enterprise-approved generative AI platforms.
- Integrate large language models with application interfaces, workflow services, APIs, and enterprise data sources.
- Apply prompt engineering, structured outputs, retrieval-augmented generation, tool use, and AI-agent patterns where appropriate.
- Develop context-aware chatbot and virtual-assistant capabilities.
- Implement output validation, error handling, fallback responses, and human escalation paths.
- Evaluate AI responses for relevance, accuracy, consistency, security, and user experience.
- Maintain clear separation between AI recommendations and authoritative business decisions.
- Monitor AI-enabled functionality and recommend improvements based on usage and performance.
- Develop recommendation capabilities that match user needs with existing software, infrastructure, services, and reusable enterprise assets.
- Design ranking and matching logic that prioritizes reuse before recommending new acquisitions or development.
- Build user interfaces that clearly explain recommendations, supporting factors, and available alternatives.
- Develop prioritization workflows based on business value, impact, urgency, risk, complexity, and implementation effort.
- Create AI-assisted risk-assessment modules supporting business, technical, security, and operational review.
- Integrate recommendations and risk findings into request-routing and approval workflows.
- Capture user feedback to improve recommendation quality and relevance.
- Maintain documentation describing recommendation rules, assumptions, data sources, and limitations.
- Develop integrations with enterprise workflow, request-management, and service-management platforms.
- Build automated workflows covering intake, classification, routing, assignment, review, approval, escalation, and closure.
- Develop APIs and data-exchange components connecting the solution with enterprise systems.
- Configure notifications, status updates, audit trails, business rules, and exception handling.
- Integrate application functionality with ServiceNow or comparable workflow platforms.
- Troubleshoot workflow, connectivity, authentication, data-mapping, and synchronization issues.
- Ensure integrations are secure, observable, maintainable, and appropriately documented.
- Support changes to workflows and integrations as operational requirements evolved
- Design, develop, document, and maintain REST and GraphQL APIs.
- Build modular services using microservices or service-oriented architecture patterns.
- Define API contracts, request and response models, validation rules, and error-handling standards.
- Implement authentication, authorization, logging, monitoring, and secure communication.
- Develop integrations with AI services, cloud resources, enterprise applications, and data platforms.
- Create automated unit, integration, contract, and regression tests for APIs and services.
- Monitor API health, reliability, latency, and failure patterns.
- Maintain API specifications and integration-support documentation.
- Use approved AI coding assistants and pair-programming tools to support requirements analysis, code generation, debugging, testing, and documentation.
- Apply prompt-engineering techniques to improve the quality of AI-assisted development outputs.
- Validate all AI-generated code, tests, requirements, and documentation before use.
- Establish reusable prompts, development patterns, and review practices.
- Use AI tools to improve delivery efficiency while maintaining engineering quality, security, and accountability.
- Identify potential intellectual-property, privacy, security, and data-handling risks associated with AI-assisted development.
- Document significant AI-assisted technical decisions and resulting implementation changes.
- Share effective AI development practices with other team members through demonstrations and knowledge transfer.
- Develop automated unit, component, API, integration, regression, and end-to-end test suites.
- Create test coverage for front-end applications, back-end services, integrations, workflows, and AI-assisted capabilities.
- Incorporate automated testing into development and deployment pipelines.
- Develop repeatable test data, mocks, stubs, and service-virtualization components where appropriate.
- Validate functional behavior, security controls, error handling, accessibility, and performance.
- Investigate defects, determine root causes, and implement sustainable corrections.
- Maintain traceability between requirements, code changes, test results, defects, and releases.
- Support production validation and post-deployment stabilization.
- Design, configure, and maintain CI/CD pipelines in Azure DevOps or comparable delivery platforms.
- Automate application builds, testing, security checks, packaging, deployment, and post-release validation.
- Manage code through Git-based repositories using approved branching, review, and merging practices.
- Containerize applications and services using Docker.
- Support container orchestration and cloud-native deployment using Kubernetes.
- Deploy and support applications across AWS, Microsoft Azure, or comparable cloud environments.
- Implement environment-specific configuration, secret management, logging, monitoring, and rollback procedures.
- Troubleshoot build, pipeline, deployment, container, networking, and cloud-environment issues.
- Apply secure software development practices throughout design, development, testing, and deployment.
- Implement input validation, secure authentication, authorization, encryption, secrets management, and appropriate access controls.
- Integrate automated security testing and dependency scanning into CI/CD pipelines.
- Review source code and architecture for common application-security weaknesses.
- Collaborate with security and architecture teams to address identified risks.
- Maintain audit logs and technical evidence supporting security reviews.
- Ensure sensitive information is not inappropriately exposed to AI tools, application logs, prompts, or external services.
- Support remediation of security findings and validate implemented corrections.
- Create and maintain technical specifications, API documentation, architecture diagrams, deployment guides, and operational procedures.
- Document application components, integrations, dependencies, configuration settings, security controls, and data flows.
- Maintain developer onboarding and local-environment setup instructions.
- Produce troubleshooting guides and production-support documentation.
- Participate in solution demonstrations, architecture reviews, and stakeholder discussions.
- Deliver knowledge-transfer sessions to development, support, and administrative teams.
- Document technical decisions, known limitations, risks, and recommended future improvements.
- Contribute to transition planning and future expansion activities.
Required Qualifications - Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related discipline.
- Equivalent relevant professional experience may be considered in place of the degree.
- Minimum five years of professional full-stack application development experience.
- Experience with a combination of modern development technologies, such as:
- JavaScript and TypeScript
- React or Angular
- Node.js
- Python
- .NET
- Java
- Experience developing REST APIs, GraphQL APIs, and microservices-based applications.
- Experience using Azure DevOps, GitHub, Git, CI/CD pipelines, and Agile delivery practices.
- Demonstrated experience delivering MVPs, prototypes, proofs of concept, or production solutions within accelerated timelines.
- Experience designing or implementing enterprise workflow, intake management, case management, request management, or service-delivery solutions.
- Experience integrating applications with ServiceNow or a comparable enterprise workflow platform.
- Experience with cloud-native application development in AWS, Microsoft Azure, or comparable cloud environments.
- Experience with Docker, Kubernetes, containerized deployment, and distributed application architectures.
- Experience implementing AI-powered user experiences, chatbot interfaces, or LLM integrations.
- Experience with AI-native engineering practices, including:
- AI-assisted development
- Prompt engineering
- Code generation
- Automated testing
- AI agents
- AI-augmented software delivery
- Experience with AI coding assistants or AI pair-programming platforms.
- Strong understanding of secure software development and DevSecOps principles.
- Experience working in Agile or rapid MVP delivery environments.
- Strong analytical, troubleshooting, documentation, and communication skills.
- Ability to communicate technical and AI concepts to both technical and nontechnical stakeholders.
Preferred Qualifications - Experience building customer-facing intake, request-management, workflow, or self-service portals.
- Experience within ServiceNow-centered enterprise environments.
- Experience supporting government, public-sector, regulated, or similarly complex organizations.
- Experience integrating generative AI with enterprise applications and workflow platforms.
- Experience with retrieval-augmented generation, vector search, embeddings, AI agents, or conversational AI.
- Experience developing intelligent recommendation, ranking, routing, or decision-support capabilities.
- Familiarity with responsible AI, AI governance, privacy, and model-risk practices.
- Familiarity with Section 508, WCAG, or comparable digital accessibility standards.
- Experience conducting application-security testing and remediating security findings.
- Experience with monitoring, logging, observability, and performance optimization.
- Experience contributing to architecture documentation and technical roadmaps.
- Experience supporting pilot-to-production transition and operational readiness.
Required Certification Candidates should possess at least one relevant professional certification in an applicable area, such as:
- Cloud computing
- DevOps or DevSecOps
- Agile or Scrum
- AWS
- Microsoft Azure
- Google Cloud
- ServiceNow
- Kubernetes
Preferred Certifications - Microsoft Certified: Azure Developer Associate
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: DevOps Engineer Expert
- AWS Certified Developer
- AWS Certified Solutions Architect
- Google Cloud Professional Cloud Developer
- ServiceNow Certified Application Developer
- ServiceNow Certified Implementation Specialist
- Certified Kubernetes Application Developer
- Certified Scrum Developer or Certified ScrumMaster
- Relevant AI engineering or secure-development certification
