Full Stack AI Engineer

Ryan•Plano, TX

1d•Hybrid

About The Position

The Full Stack AI Engineer is a hybrid technical and client-facing role responsible for the design, development, and deployment of applications that embed generative AI. The position is grounded in full-stack engineering, including the ability to work across the full stack to design, build, and deliver complete applications. Further, it extends that foundation by integrating generative AI capabilities, including large language models, agents, and LLM-based workflows, as an integral component of the application architecture rather than a supplementary layer. The role is oriented toward delivering measurable business outcomes through robust, dependable software. This position is designed for engineers with strong full-stack fundamentals who are developing their expertise in applied generative AI. It is a generative-AI engineering role built upon full-stack fundamentals, which is distinct from a traditional machine-learning or data-science position. Working across the full delivery lifecycle: from problem discovery and system design through build, deployment, and iteration - this engineer combines strong full-stack execution with applied generative AI to deliver software that creates measurable value for clients and the business. The role calls for a strong engineering mindset, a customer orientation, and the ability to translate complex problems into AI-powered, full-stack software.

Requirements

A bachelor’s or master’s degree in computer science, Engineering, AI/ML, or a related field (or equivalent relevant work experience).
A foundation in software engineering, with exposure to machine learning or solutions engineering environments.
Exposure to, or strong interest in, integrating generative AI including large language models and agent frameworks into applications that serve users.
Familiarity with, or demonstrated interest in, building agent-based solutions using frontier language models.
Proficiency in at least one general-purpose programming language, such as Python or TypeScript/JavaScript.
Demonstrated experience building a full-stack application end to end—for example, a component-based front end (such as React) backed by an API service (such as a Node/TypeScript or Python/FastAPI backend).
A solid grasp of engineering fundamentals: HTTP/REST APIs, data modeling and databases, version control (Git), testing, and debugging.
Conceptual familiarity with system architecture and cloud platforms (e.g., AWS, Azure, or GCP), and a general understanding of what it takes to make an application available to real users - that is, serving people other than yourself, not just running on your own machine.
Experience integrating, or a strong interest in integrating, generative AI into applications, including large language models, agent frameworks (e.g., LangChain, LangGraph, AutoGen, or similar), and vector databases.
Familiarity with AI-assisted (“agentic”) coding tools such as Claude Code or Codex.
Valid driver’s license required.

Nice To Haves

Client-facing or consulting-style experience is a plus.

Responsibilities

Delivers AI-powered, full-stack software solutions in coordination with end users and business professionals.
Serves as a technical point of contact for clients, building strong relationships with both technical and non-technical stakeholders.
Shares insights and best practices from client engagements to improve internal tools, playbooks, and direction.
Collaborates with business professionals and customers to understand their needs and challenges, and to help identify high-value use cases for AI-powered software.
Contributes to the end-to-end delivery of AI-powered, full-stack software solutions, including discovery, scoping, system design, development, and rollout to real users.
Builds and deploys full-stack applications with generative AI engineered into the software, such as LLM-based workflows and agent-based systems within client infrastructure.
Works within client and business environments to support successful adoption, performance, and measurable business outcomes.
Delivers AI-powered software that serves real users, addresses real-world problems, and generates tangible ROI.
Supports continuous improvement by iterating on deployed solutions and optimizing performance and user adoption.
Balances speed, scope, and quality to ensure timely and effective delivery of high-impact projects.
Makes extensive use of AI-assisted coding and engineering workflows to deliver scalable, performant software.