AI Engineer - W2 position

Job Title: AI Engineer – Agentic AI, Ontology & Enterprise Application Platforms

McKinney, TX - Onsite

Position Overview

We are seeking an AI Engineer with experience designing and developing agentic AI solutions, ontology-driven applications, and intelligent enterprise application platforms. The ideal candidate will have expertise in AI skill orchestration, product and capability decomposition, knowledge modeling, and integrating AI agents with enterprise systems.

This role will focus on translating complex business products and workflows into reusable AI skills, defining relationships between business concepts through ontologies, and orchestrating intelligent agents to execute enterprise processes. The candidate should understand how AI capabilities can be embedded into enterprise applications to deliver contextual, scalable, and adaptive user experiences.

Experience with agentic architectures, ontology-based knowledge systems, skill orchestration, and Server-Driven UI (SDUI) will be highly valuable.

Key Responsibilities

• Agentic AI Development: Design and implement AI agents capable of reasoning, planning, tool execution, and coordinating multi-step business workflows.
• AI Skill Orchestration: Develop mechanisms to define, register, discover, sequence, and orchestrate reusable AI skills, tools, and services to support complex business processes.
• Ontology-Driven Application Design: Build and maintain ontologies, semantic models, knowledge graphs, and relationships between business entities, products, capabilities, and processes.
• Product and Capability Decomposition: Break down enterprise products, business processes, and application capabilities into modular, reusable skills that AI agents can invoke dynamically.
• Enterprise Application Integration: Integrate AI agents and skills with enterprise application platforms, APIs, business services, and existing systems to enable end-to-end workflow automation.
• Context and Knowledge Management: Design mechanisms for contextual retrieval, knowledge grounding, and maintaining consistency between business terminology, domain models, and AI-generated decisions.
• AI Workflow Engineering: Develop multi-agent or agentic workflows with appropriate task routing, dependency management, state handling, error recovery, and human-in-the-loop controls.
• SDUI and Dynamic User Experiences: Understand Server-Driven UI concepts and how AI-driven decisions or workflow states can dynamically determine application screens, components, and user interactions.
• Quality, Evaluation, and Governance: Implement testing and evaluation strategies for agent behavior, skill execution, retrieval accuracy, and workflow reliability, including security and access controls.
• Cross-Functional Collaboration: Work with product managers, enterprise architects, domain experts, UX engineers, and platform teams to translate business requirements into scalable AI-powered solutions.

Required Skills and Qualifications

• Experience in AI engineering, agentic application development, intelligent automation, or enterprise application engineering.
• Strong understanding of AI agent architectures, tool/function calling, task planning, and multi-step workflow orchestration.
• Hands-on experience designing or implementing reusable AI skills, tools, capabilities, or modular business services.
• Knowledge of ontology modeling, semantic data structures, domain models, knowledge graphs, or ontology-driven application architectures.
• Ability to decompose complex products and business processes into capabilities, components, and reusable execution skills.
• Experience integrating AI solutions with enterprise applications, REST APIs, microservices, and business platforms.
• Understanding of large language models (LLMs), retrieval-augmented generation (RAG), embeddings, and context-grounding techniques.
• Familiarity with structured data, metadata, entity relationships, and knowledge representation.
• Understanding of enterprise application architecture, including service-oriented or microservices-based systems.
• Familiarity with SDUI or dynamic UI composition is an advantage.
• Strong problem-solving, systems-thinking, and communication skills.

Preferred Technical Experience

• Agent frameworks and orchestration tools such as LangGraph, Semantic Kernel, AutoGen, or comparable technologies.
• LLM platforms and APIs, including Azure OpenAI, OpenAI APIs, or equivalent services.
• Knowledge graph and ontology technologies such as RDF, OWL, SPARQL, Neo4j, or equivalent solutions.
• Programming experience in Python, TypeScript, JavaScript, Java, or similar languages.
• API integration, event-driven architectures, workflow engines, and enterprise service platforms.
• Familiarity with vector databases, semantic search, RAG pipelines, and enterprise knowledge retrieval.
• Exposure to Server-Driven UI frameworks, schema-driven interfaces, or dynamic component rendering.
• Experience with enterprise security, identity management, observability, and responsible AI practices.

Ideal Candidate Profile

The ideal candidate combines AI engineering with enterprise architecture and domain modeling. They should be able to understand a business product, identify its capabilities, represent domain knowledge through an ontology or semantic model, decompose the capabilities into reusable AI skills, and orchestrate those skills through agents integrated with enterprise applications.

Candidates who have built ontology-driven or metadata-driven platforms, agentic workflow systems, reusable AI capability frameworks, or intelligent enterprise applications will be particularly well suited for this role.