Software Engineer III

Clayton•Maryville, TN

34d

About The Position

As a Software Engineer on the AI Delivery Team, you will build and maintain applications and services that integrate with AI components and enterprise infrastructure including the MCP Gateway and MCP Servers. You will contribute across the SDLC, collaborate with cross-functional teams, participate in Agile Scrum ceremonies, and deliver reliable, well‑tested software solutions. You will partner with AI Engineers to integrate LLM/AI services into enterprise applications, ensuring integrations are secure, resilient, and scalable. You will work directly with Team Leads, Business Users, Product Owners, Functional Analysts, and other Engineers to build new projects and support existing applications. The ideal contractor is self-motivated, able to multi-task moderately complex issues, and brings a strong background in cloud (especially AWS), microservices, and modern front-end frameworks. Experience with AI code generation tools (such as GitHub Copilot or Claude), test automation, and IDE extension development is highly valued. About The Team You will join the AI Delivery Team within the Enterprise Enablement organization, a group focused on accelerating Clayton Homes’ adoption of AI, automation, and next-generation digital capabilities. Our team partners across the enterprise to design, build, and integrate AI-powered solutions, modernize legacy systems, and enable business units to leverage cloud, microservices, and advanced analytics.

Requirements

Bachelor’s Degree in Computer Science, related field, or equivalent experience.
7+ years of software development experience.
Experience consuming AI/LLM APIs and integrating AI services.
Experience with MCP Gateway and MCP Server integrations.
Experience with Agile, Jira, source control systems, and CI/CD pipelines.
Hands-on experience with AWS or other cloud platforms.
Experience with microservices, containerization (e.g., Docker), and RESTful APIs.

Nice To Haves

Experience with AI code generation tools (e.g., GitHub Copilot, Claude) preferred.
Excellent time management and ability to prioritize work with little direction.
Strong analytical skills; ability to weigh pros and cons of design choices and make recommendations.
Adaptability to changing processes and technologies.
Collaborative mindset, fostering understanding of mutual goals.
Understanding of vector databases, embeddings, or prompt-based workflows.
Experience with microservices, distributed systems, and event-driven architectures.
Ability to adapt quickly to changing technologies and AI patterns.
Excellent analytical and communication skills.

Responsibilities

Serve as a technical lead and liaison to business and engineering teams.
Turn functional requirements into actionable application and service designs.
Build services and APIs that integrate with AI inference endpoints via MCP Gateway and connect downstream to MCP Servers.
Develop backend or full‑stack components supporting RAG workflows, LLM calls, and AI-enabled business logic.
Ensure fault‑tolerance, observability, and security across AI-integrated applications.
Write high-quality code, emphasizing working software over gold-plated solutions.
Conduct root cause analysis for defects—including MCP routing issues and AI service failures.
Build automated tests, unit tests, and integration tests covering AI interaction paths.
Participate in full SDLC—from requirements to testing to deployment.
Promote collaboration, innovation, and technical excellence across the team.
Mentor junior team members through peer reviews and provide guidance on story implementation.
Lead architecture and system integration efforts, ensuring seamless connections between systems.
Identify and address security concerns in design and implementation, reducing friction while maintaining compliance.
Design and implement cloud-native solutions, with a focus on AWS services and infrastructure.
Develop and containerize microservices to support scalable, repeatable AI and business processes.
Participate in the full SDLC from requirements gathering to testing and delivery.
Break down stories into small, testable units focused on rapid delivery.