AI Software Engineer Intern

Praxent

2h•$15 - $15•Remote

About The Position

As an AI Software Engineering Intern at Praxent, you will be a vital part of our mission to integrate AI seamlessly into legacy software modernization projects, our client’s platforms, and our overall agentic SDLC. Your role involves collaborating closely with cross-functional teams, including engineering, product management, and client success, to understand the unique challenges faced by our clients and translate these insights into innovative proofs of concept and AI development tools. You will embrace a hands-on approach to experimentation and iteration, contributing to the creation of actionable AI strategies that align with our clients' business objectives. By developing agentic AI tools such as prompts, templates, commands, and hooks/skills, you will help streamline the integration of AI technologies, driving efficiency and enhancing the quality of deliverables. As you engage in iterative processes, your commitment to quality oversight and attention to detail will ensure that our projects maintain high standards of completeness and consistency. You will also play a role in fostering a culture of continuous learning and improvement within the team, actively participating in knowledge-sharing initiatives to elevate our collective expertise in AI advancements. Through your contributions, Praxent will further establish itself as a trusted partner for organizations looking to harness the transformative power of AI. Your work will not only lead to successful project outcomes but also enhance client satisfaction and support sustained business growth, ultimately positioning Praxent as a leader in the evolving landscape of financial technology.

Requirements

Foundational AI Literacy: Ability to understand and apply basic AI concepts—such as how Large Language Models work, LLM harnesses like Claude Code, and the principles of RAG—to solve simple automation tasks or data queries.
Logic-Based Data Mapping: Ability to analyze structured and semi-structured data from legacy sources to identify patterns and relationships, ensuring that information is accurately translated and formatted for use within AI-driven applications.
Pragmatic Scripting Skills: Experience in a core language like Python or TypeScript to write clean, functional scripts that automate data handling and connect different software components.
Creative Prompt Design: Capability to iteratively experiment with and refine structured AI prompts to achieve desired outcomes, demonstrating a blend of logical thinking and clear communication. Test and improvement of prompts
Technical Curiosity & Adaptability: A strong drive to explore new AI tools, libraries, and frameworks, with the ability to quickly learn and apply them to specific project challenges as they arise.
Detail-Oriented Debugging: Ability to systematically test code and AI outputs, identifying inconsistencies or errors and working collaboratively to refine the logic until it meets quality standards.

Responsibilities

Development of Proofs of Concept (PoCs)
Support implementation of innovative PoCs that demonstrate the practical applications of AI in modernizing legacy systems or integrating AI into software solutions.
Collaborate with senior engineers to conceptualize, design, operate and execute PoCs.
Utilize AI models to simulate enhancements and document findings.
Development of AI Tools
Work from a defined backlog to develop prompts, templates, commands, hooks/skills, and other tools to support AI initiatives.
Participate in our AI Center of Excellence brainstorming sessions to identify essential tools for AI workflow efficiency.
Iterate on feedback from team members to refine tools and templates.
Help source tooling and AI-enabled needs from delivery teams.
Iterative Experimentation and Improvement
Engage in experimentation to improve AI outputs and processes.
Conduct experiments using different AI models and configurations.
Analyze results and document insights for future reference.
Collect and share within our AI Center of Excellence through regular presentations of experimental findings, directly resulting in measurable improvements to AI output consistency.
Tool Chain Set-Up
Configure and deploy project-specific AI toolchains.
Act as the "Tooling Architect" for specific projects, selecting and configuring the right sequence of AI agents and development utilities to bridge the gap between legacy codebases and modern environments.