Software Engineer, Internal AI Systems

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Full Time Professional Research Commons, Orlando, FL, US

6 days ago Requisition

Salary Range: $92,000.00 To $ Annually

Are you interested in working for an industry leader named one of the Orlando Sentinel's Top 100 Workplaces in Orlando for the 5th year in a row?

The DiSTI Corporation, located near the University of Central Florida, is a leading provider of graphical user interface software and customized 3D training solutions. Since 1994, our software products and professional services have pioneered the advancement of user experience for Fortune 500 companies, the U.S. military, and clientele from around the globe. Our software serves the automotive, aerospace, medical, and training markets, and continues to expand into new markets as consumers increasingly expect a consistent device experience across all facets of their daily lives.

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This is an onsite role in our Orlando office.

Role Overview

We are looking for a mid-level Software Engineer to maintain, improve, and expand our internal AI software stack. This role will inherit an existing in-house AI environment and continue building on it to improve developer productivity, knowledge access, workflow automation, and internal business operations.

This is an applied engineering role, not machine learning research. The ideal candidate is a strong software engineer who is deeply curious about modern AI tools—LLMs, RAG systems, agents, code assistants, and automation platforms. They experiment with AI tools independently, understand the current landscape, and can clearly explain what AI is useful for, what it isn't, and how to apply it safely in an environment handling sensitive data.

They thrive with ambiguity, actively explore new technology, and balance enthusiasm with healthy skepticism about AI's capabilities.

The role works closely with the Chief Engineer and engineering leadership to maintain the current AI stack, identify opportunities for improvement, build new AI-powered workflows, and help other engineers adopt AI tools effectively.

The ideal candidate is a curious and practical builder who can independently identify opportunities where AI or automation could improve workflows. They communicate ideas clearly, teach other engineers how to use new tools, and explain technical tradeoffs to leadership. Success means maintaining and improving our internal AI systems, building useful tools that others actually use, and keeping the company current with practical AI developments without chasing hype.

Key Responsibilities:

• Maintain and improve the company's internal AI software stack, including chatbots, RAG-based knowledge bases, AI agents, code assistance tools, and workflow automation.
• Build internal tools and automation that improve engineering, management, documentation, code review, knowledge retrieval, and other business workflows.
• Work with on-premises and controlled AI infrastructure (including Anthropic and OpenAI models) where sensitive or government-related data must remain within company-controlled systems.
• Maintain Docker-based deployments and internal Linux server environments.
• Support AI model endpoints, LLM routing, usage tracking, cost awareness, and API integrations.
• Help manage internal knowledge systems using documents, code repositories, project management data, and other company sources.
• Evaluate new AI tools, frameworks, and workflows and determine whether they are practical, secure, and useful for the company.
• Communicate AI capabilities, limitations, and best practices to engineers and non-AI stakeholders.
• Work independently on ambiguous problems by researching, prototyping, using AI tools effectively, and turning ideas into working internal solutions.
• Help identify new areas across the company where AI, automation, or better tooling could reduce manual work or improve quality.

Required Qualifications:

• 2–3+ years of professional software engineering experience.
• Degree in Computer Science, Software Engineering, or a related technical field.
• Experience building backend services, APIs, internal tools, or integrations.
• Experience with Docker, Linux server environments, and relational databases (PostgreSQL or similar).
• Strong curiosity about modern AI tools and a demonstrated habit of experimenting with new technologies independently.
• Ability to communicate clearly with engineers, managers, and other stakeholders.
• Personal AI projects, Git Hub repos, demos, prototypes, or examples of independent experimentation.

Preferred Qualifications:

• Understanding of LLM systems including RAG architecture, embeddings, vector search, agents, retrieval quality evaluation, tool calling, and prompt engineering.
• Experience with on-premises AI platforms, local LLMs, RAG implementations, model routing, usage tracking, or controlled data environments.
• Familiarity with…