Full Stack AI Solution Developer

Donatech Corporation — United States · Posted ~2 hours ago

Skills

full-stack development AI/LLM integration frontend development backend development Model Context Protocol database design data engineering Docker Kubernetes OpenShift GitLab CI/CD agent workflows AI/LLM databases

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Summary ✨ AI‑Generated

A full-stack engineering opportunity focused on turning AI prototypes into production-ready enterprise capabilities. You will develop frontend and backend services, integrate LLMs and agent workflows, build data and database solutions, deploy containerized applications, and maintain automated source-control and CI/CD practices.

Highlights

Build AI-enabled solutions from prototypes into operational systems while working across frontend, backend, data, and infrastructure layers. The role offers hands-on work with LLMs, agent workflows, containerized applications, databases, and automated CI/CD pipelines.

Description

Position would require the candidate to be a W2 employee of Donatech. US Citizenship Required. • Development of AI-enabled solutions from prototype/proof-of-concept maturity into operational capability. • Develop, maintain, and troubleshoot frontend applications and backend services. • Support development and sustainment of Model Context Protocol (MCP) services. • Support integration of AI/LLM capabilities, agent workflows, and model/tool interactions into enterprise applications. • Design and implement database and data engineering capabilities, including selection of appropriate database technologies, schema/data model design, data access patterns, and integration with application services. • Develop and maintain containerized applications using Docker and deploy solutions to OpenShift/Kubernetes environments. • Build and maintain GitLab repositories, branching strategies, merge request practices, and GitLab CI/CD pipelines that automate build, test, containerization, and deployment activities. • Automate deployment to OpenShift using Helm charts and related deployment/configuration management practices. • Integrate applications with enterprise authentication and authorization patterns • Identify and troubleshoot application, container, deployment, integration, authentication, database, and performance issues across the full stack. • Develop and maintain technical documentation, operational procedures, troubleshooting guides, and sustainment handoff materials. Basic Qualifications: • 5+ years of experience performing full stack software development. • Hands-on experience developing and sustaining frontend and backend services. • Experience using GitLab for source control, code review, merge requests, and team-based software development. • Experience deploying, operating, or troubleshooting applications in OpenShift or Kubernetes environments. • Experience with modern software delivery practices, including automated build, test, packaging, containerization, and deployment. • Experience with data engineering concepts, data modeling, data persistence, and application/database integration. • Knowledge of OAuth 2.0 or similar authentication and authorization patterns. • Ability to review and understand code developed by others, including AI-assisted code, and determine whether it is correct, maintainable, secure, and operationally supportable. Desired Skills: • Experience with observability, logging, monitoring, and troubleshooting tools. • Familiarity with ServiceNow or similar IT service management platforms. • Experience developing, integrating, or sustaining AI-enabled applications, LLM-based solutions, agentic workflows, or AI-assisted automation capabilities. • Familiarity with LangGraph or similar Python-based agentic frameworks. • Familiarity with Model Context Protocol (MCP), tool calling, skills, agents, prompts, context windows, token usage, and LLM application design considerations. • Highly adaptive to priority changes, able to work independently, and able to balance rapid development with long-term maintainability and sustainment. • Familiarity with AI/ML infrastructure, responsible AI considerations, and practical limitations of LLM-based systems.