Applied AI Intern

About Marloa

Marloa is building AI-powered products that help businesses automate customer interactions and operational workflows.

About the Role

We're looking for an AI/ML Engineer Intern who is excited about building practical AI systems using modern language models, RAG pipelines, and agent workflows. This is an applied AI role: you won't be doing research in isolation, you'll be building, testing, and shipping AI features used in real products.

You'll work closely with the engineering team, with hands-on ownership of the AI layer of the product.

What You'll Work On

1. Build prototypes and product features using LLM APIs
2. Work with models from providers such as OpenAI, Anthropic, Gemini, or similar
3. Build and test AI agent workflows, including tool/function calling and structured outputs
4. Implement and work with MCP (Model Context Protocol) servers/connectors to link AI agents to external tools and data sources
5. Build RAG and knowledge-retrieval workflows
6. Work with embeddings and vector databases
7. Connect AI systems with APIs and external tools
8. Test prompts, model outputs, and agent behavior for reliability and correctness
9. Help evaluate AI quality, reliability, latency, and cost
10. Build small Python services and backend components
11. Research and test new AI models, frameworks, and techniques
12. Work with the engineering team to move successful prototypes into production

What We're Looking For

1. Good knowledge of Python
2. Strong interest in AI, machine learning, and LLMs
3. Working knowledge of MCP (Model Context Protocol)
4. Basic understanding of APIs and backend development
5. Familiarity with Git and GitHub
6. Strong problem-solving and debugging ability
7. Ability to learn new technologies independently
8. Comfortable experimenting and building quickly
9. A portfolio of personal AI/ML projects, GitHub work, university projects, or hackathon work that demonstrates real ability — not just tutorial-following

Nice to Have

1. Experience with OpenAI, Anthropic, Gemini, or similar APIs
2. Experience with RAG, embeddings, or vector databases
3. Experience building AI agents or tool-calling workflows
4. Experience with LangChain, LlamaIndex, or similar frameworks
5. Experience with FastAPI, Flask, or similar Python frameworks
6. Basic machine learning knowledge
7. Experience deploying AI applications
8. Participation in hackathons, open-source projects, or AI-focused projects

We care more about your ability to build, experiment, learn quickly, and solve problems than your degree or prior job experience. No formal experience is required for this role, but strong demonstrated AI/ML projects and Applied AI knowledge are non-negotiable.