Agentic AI Engineering Intern - Undergraduate

Deion is the done-for-you, anti-AI-slop Canva alternative for YouTube thumbnail design. They are looking for an undergraduate Agentic AI Engineering Intern to design and implement custom autonomous agent architectures and collaborate with teams to scale AI solutions.

Responsibilities:

• Design and implement custom autonomous agent architectures using Python and Google Cloud infrastructure
• Leverage Gemini CLI and Claude Code to accelerate development cycles and orchestrate complex agentic task execution
• Build bespoke reasoning loops and decision-making logic to solve open-ended problems without relying on pre-built low-code platforms
• Integrate diverse data sources and external services into a cohesive system via robust, Python-based middleware
• Debug and refine non-deterministic agent behaviors to ensure reliability, safety, and alignment with user goals
• Architect modular systems where agents can autonomously select and use the correct tools for a given task
• Collaborate with cross-functional teams to define, implement, deploy, and scale AI solutions within a cloud-native environment
• Document system designs, source code, workflow designs, research, development plans, and engineering standards to enable solution modularity, reproducibility, portability, collaboration, and knowledge sharing
• Collaborate with teammates using Slack, Notion, Google Meet, and/or other software

Requirements:

• Completion of a demo project
• At least 10 hours of synchronous meeting availability between 1:00 PM and 5:00 PM Central Time on weekdays for the next 6 months
• Comfortable working 100% remotely as part of a distributed team
• Excel at designing autonomous systems that can reason through complex tasks using LLMs
• Work products demonstrate a high level of attention to detail and the ability to see the big picture
• Aptitude, mindset, and ability to learn quickly make up for shortcomings in current skill set
• Resonate with MDP's core values: High performance, engagement, and reliability; Proper prioritization, timeliness, and risk mitigation; Outcome ownership, accountability, and meritocracy; Systems thinking, modularity, and automation; Excellence in execution and hyper-detail orientation; Documentation and asynchronous work; Resourceful initiative
• Design and implement custom autonomous agent architectures using Python and Google Cloud infrastructure
• Leverage Gemini CLI and Claude Code to accelerate development cycles and orchestrate complex agentic task execution
• Build bespoke reasoning loops and decision-making logic to solve open-ended problems without relying on pre-built low-code platforms
• Integrate diverse data sources and external services into a cohesive system via robust, Python-based middleware
• Debug and refine non-deterministic agent behaviors to ensure reliability, safety, and alignment with user goals
• Architect modular systems where agents can autonomously select and use the correct tools for a given task
• Collaborate with cross-functional teams to define, implement, deploy, and scale AI solutions within a cloud-native environment
• Document system designs, source code, workflow designs, research, development plans, and engineering standards to enable solution modularity, reproducibility, portability, collaboration, and knowledge sharing
• Collaborate with teammates using Slack, Notion, Google Meet, and/or other software
• Strong proficiency in Python with a focus on writing clean, scalable, and highly modular code
• Practical experience deploying and managing applications within the Google Cloud ecosystem
• Familiarity using advanced developer tools like Gemini CLI or Claude Code to solve technical challenges
• Demonstrated systems thinking skills with the ability to map out intricate logic flows and manage state across complex processes
• Ability to decompose high-level, ambiguous objectives into a series of small, autonomous, and verifiable tasks
• Experience using Git-based version control to manage codebases within a collaborative team setting
• Excellent communication skills for explaining technical trade-offs and architectural decisions to both technical and non-technical peers
• A high degree of adaptability and the 'engineer's intuition' required to troubleshoot probabilistic systems
• Effective modular thinking skills evidenced by the ability to break down large, intricate undertakings into a series of smaller self-contained tasks
• Excellent written and verbal communication skills
• Emotional intelligence and coachability