GenAI Engineer Intern

Job Title: Generative AI Engineer Intern

Location: Remote / Hybrid (US-based preferred)

Duration: 3–6 Months (extendable)

Compensation: Paid or Unpaid (based on experience & availability)

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We go beyond courses and tutorials. At Cerebrone.ai, interns and members build real GenAI systems—AI agents, RAG pipelines, memory layers, evaluation frameworks, and model-serving infrastructure—using production-grade tools and architectures.

Our community spans students, working professionals, startup founders, and enterprise engineers across the U.S. and globally. We emphasize:

• Learning by building

• Systems thinking over prompt hacking

• Open-source, research-backed GenAI engineering

• Agent-based architectures, not chatbots

Role Overview

We are seeking a Generative AI Engineer Intern who wants hands-on experience building agentic AI systems end-to-end.

You will work with the AgentVersity engineering team to design, build, and evaluate:

• AI agents

• LLM and SLM-based applications

• Memory-enabled systems

• RAG pipelines

• Model hosting and inference stacks

This role is ideal for candidates who want to deeply understand how GenAI systems work in practice, from architecture to deployment.

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What You’ll Learn & Work On

• Build applications using Large Language Models (LLMs) and Small Language Models (SLMs)

• Design and implement AI agents with tools, memory, and reasoning

• Work with LangGraph and/or Google Agent Development Kit (ADK)

• Build Retrieval-Augmented Generation (RAG) pipelines

• Understand agent memory (short-term, long-term, vector-based)

• Host and serve models using vLLM

• Compare closed vs open-source models

• Run LLM evaluations (quality, latency, cost, hallucination analysis)

• Learn tradeoffs in accuracy vs speed vs cost

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Responsibilities

• Assist in building GenAI-powered agents and applications

• Implement RAG pipelines using documents, embeddings, and retrievers

• Experiment with open-source LLMs and SLMs

• Support model inference and hosting using vLLM

• Design agent workflows using LangGraph or Google ADK

• Evaluate model outputs and improve system performance

• Document architectures, experiments, and learnings

• Collaborate with engineers on debugging and optimization

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Required Skills (Beginner to Intermediate)

• Strong interest in Generative AI and AI agents

• Programming experience in Python

• Basic understanding of:

• LLMs and transformers (conceptual level)

• Prompting and text generation

• Familiarity with Git and GitHub

• Curiosity, ownership, and willingness to learn fast

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Nice-to-Have Skills

• Experience with LangChain / LangGraph

• Exposure to Google ADK

• Knowledge of RAG architectures

• Familiarity with vector databases (FAISS, Chroma, Pinecone, etc.)

• Experience running models locally or on cloud GPUs

• Interest in evaluations, benchmarks, or model optimization

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Eligibility & Work Authorization

• US Citizens or Green Card holders preferred

• OPT and CPT candidates are welcome to apply

• Must be legally authorized to work in the U.S. (for paid roles)

• For unpaid roles, academic credit alignment is acceptable

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Who Should Apply

• Students or recent graduates in CS, AI, ML, or related fields

• Engineers transitioning into GenAI / AI agent roles

• Self-learners with GenAI projects or research interest

• Candidates who want deep engineering exposure, not surface-level demos

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What You’ll Gain

• Hands-on experience with real GenAI systems

• Exposure to agentic architectures used in industry

• Mentorship from engineers actively building AI products

• Portfolio-ready projects (agents, RAG systems, evaluations)

• Strong foundation for GenAI Engineer / AI Agent roles

• • Opportunity for full-time conversion or extended collaboration