Full Stack AI Software Developer

3 days ago

Savannah GA, Chatham County, GA; Georgia, United States Griting Full-time

Senior Full-Stack AI Engineer (React / Java / Python / Agentic AI)

Hiring Organization: Griting AI

Position Type: Full-Time, US remote role, directly hiring with Gritings client

Experience: 5–10 Years

Industry: Enterprise AI / SaaS / Industrial Technology

Employment Relationship: Direct employment with our client

Salary Range : 160k-180k per year(base) + performance based equity or bonus.

About the Opportunity

Griting AI is a Silicon Valley-based AI-powered talent intelligence, evaluation, and recruitment company. We partner with organizations to identify, assess, and connect exceptional technical talent with meaningful career opportunities.

We are recruiting on behalf of one of our enterprise technology clients seeking an experienced Senior Full-Stack AI Engineer to join its platform engineering team.

Our client is developing an advanced, ontology-driven enterprise AI platform that transforms complex industrial and engineering documents into structured, actionable knowledge. The platform combines modern web applications, cloud-native backend services, knowledge representation, and agentic AI capabilities.

This is a full-time opportunity with our client, not an internal engineering position at Griting AI. Griting AI serves as the recruitment and talent evaluation partner throughout the hiring process. The selected candidate will join and work directly with the clients engineering organization.

The clients identity will be shared with qualified candidates at an appropriate stage of the recruitment process.

Key Responsibilities

  • Own customer-facing features end to end, from requirements and technical design through implementation, testing, deployment, and production support.
  • Build and maintain modern web applications using React, Next.js, and TypeScript.
  • Develop scalable backend services using Java, Spring Boot, Python, and FastAPI.
  • Integrate LLMs, AI agents, RAG/GraphRAG, and multi-model orchestration into production applications.
  • Work with ontology-driven data models, knowledge graphs, and document intelligence capabilities.
  • Leverage AI-assisted development tools to accelerate implementation while maintaining engineering quality, security, and maintainability.
  • Build, containerize, and deploy cloud-native services using Docker, Kubernetes, and Helm.
  • Support production deployments across Google Cloud and Microsoft Azure.
  • Develop automated tests, troubleshoot complex issues, improve system performance, and maintain service reliability.
  • Collaborate with cross-functional stakeholders to translate business requirements into reliable software solutions.

Required Qualifications

  • 5–10 years of professional software engineering experience in full-stack or backend-focused development.
  • Strong proficiency in at least one of TypeScript, Java, or Python, with the ability to work across multiple technology stacks.
  • Experience developing production web applications using React, Next.js, or similar frameworks.
  • Experience building backend applications and REST APIs using Spring Boot, FastAPI, or equivalent frameworks.
  • Solid understanding of relational databases, authentication, authorization, and modern software architecture.
  • Hands-on experience with Docker, Kubernetes, and a major cloud platform such as GCP or Azure.
  • Demonstrated ability to use AI coding assistants effectively while independently validating generated code.
  • Proven experience owning software features from design to production deployment.
  • Strong foundation in testing, debugging, code reviews, and maintainable software development.
  • Ability to become productive quickly in unfamiliar, complex codebases.

Preferred Qualifications

  • Experience developing production AI applications involving LLMs, AI agents, RAG, GraphRAG, or multi-model workflows.
  • Familiarity with OpenAI, Anthropic Claude, Google Vertex AI, Azure OpenAI, or Model Context Protocol (MCP).
  • Experience with knowledge graphs, ontology-dri