17 ott - Bardi
Consoo
As an AI Engineer Intern, you will not be doing busywork.
You will be hands-on with our production software pipelines, helping to design, test, and scale our core machine learning models and generative AI systems.
This role is ideal for a student or recent graduate eager to bridge the gap between academic theory and high-impact industrial AI deployment.
Key Responsibilities
Model Development & Prototyping:
Assist in building and refining pattern recognition models, neural network architectures, or agentic workflows (e.g., LangChain, LlamaIndex).
Pipeline Infrastructure:
Collaborate with data and software engineers to develop scalable data pipelines for curation, automated labeling, and model evaluation.
Generative AI & RAG:
Help build and optimize Retrieval-Augmented Generation (RAG) architectures, minimizing model hallucinations and improving response retrieval using embeddings and re-ranking.
Testing & Quality Assurance:
Create and execute automated and human-in-the-loop evaluation frameworks to benchmark model accuracy, performance, and safety guardrails.
Cross-functional Collaboration:
Participate in code reviews, technical documentation, and standups to ensure robust integration of AI components into production systems.
What We Are Looking For
Educational Background:
Currently pursuing or recently graduated with a degree (BS, MS, or PhD) in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
Core Programming:
Strong proficiency in Python and hands-on experience with modern backend or ML frameworks (e.g., FastAPI, PyTorch, TensorFlow, or JAX).
AI/ML Fundamentals:
Solid understanding of machine learning basics, Natural Language Processing (NLP), or Computer Vision.
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