Generative AI Engineer (Intern)

Mactores is the agent-native AWS modernization firm. Most modernization work doesn't ship, it stalls in pilots, slips a year, or lands at three times the budget. We exist to ship it: production systems running, legacy retired, outcomes measured. Our delivery is built on Aedeon, the agent platform built by Mactores' founders' sister company, which absorbs the repetitive 60–70% of engagement work, discovery, dependency mapping, validation, test generation, that traditional consulting bills human hours against. Forward-deployed engineers own the rest: architecture, judgment, and cutover, on dates we commit to in the contract.

This internship sits alongside our Forward-Deployed Engineers (FDEs), who embed with customer teams and ship production agentic AI systems on AWS. You won't carry a delivery commitment on day one instead, you'll work under an FDE's mentorship to learn how agentic systems get built, tested, and verified against real customer data, and you'll get structured exposure to the judgment calls that sit behind every shipped feature. This is a learning-and-building role, not a staff-augmentation seat and not an advisory role. You'll write real code that feeds into real engagements, under close supervision.

Support delivery of agentic AI and AWS modernization work across our three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps. Help build and test components of AI agents, orchestration, retrieval pipelines, evaluation harnesses under FDE guidance, working with real (not demo) data wherever possible. Assist in converting existing product or process functionality into callable agent tools, under supervision. Sit in on architecture sessions and customer discussions to learn how design decisions get made and defended; you'll observe and contribute, not lead. Help document agent decisions and test evidence so outputs are traceable and defensible. Bring back what you learn from shadowing engagements into small improvements you can own.

Currently pursuing, or recently completed, a degree in Computer Science, Engineering, or a related field. A foundation in Python (and ideally some TypeScript) from coursework, personal projects, or prior internships no professional production experience required. Genuine curiosity about agentic AI: how agents plan, use tools, retrieve context, and get evaluated. You don't need to have shipped an agent, you need to want to learn how they're built properly. Basic familiarity with cloud concepts; AWS exposure through coursework, certifications-in-progress, or personal projects is a plus. Comfort with testing-driven habits, willingness to learn to write test cases before code, not after.

Personal projects or coursework involving LangGraph, CrewAI, Strands Agents, or similar orchestration frameworks. Exposure to RAG concepts chunking, embeddings, retrieval, even at a conceptual or academic level. AWS certifications in progress (Cloud Practitioner, Solutions Architect Associate, or similar).