Data Science / ML Engineer Intern

About Fractics

Fractics builds production-grade Agentic RAG and LLM solutions that power enterprise automation and modern customer experiences. As an intern, you’ll work with real datasets, real systems, and real deployments-not toy academic projects. This role is ideal for someone who wants to learn the full lifecycle of building and shipping AI systems. Experience or interest in recommender systems is a strong plus.

What You’ll Work On

• Designing and improving Agentic RAG pipelines (chunking, embeddings, retrieval, reranking)
• Cleaning, structuring, and enriching enterprise data for AI workflows
• Reading and implementing cutting-edge AI research
• Building LLM workflows, evaluators, and orchestration logic
• Working with vector databases like Pinecone, Chroma, and MongoDB Atlas Search
• Experimenting with embeddings, transformers, and semantic search
• Deploying ML components using FastAPI, Docker, and cloud environments
• Running experiments to improve grounding, reduce hallucinations, and optimize latency
• Exploring advanced systems such as modern recommender engines integrated with agentic AI

What We’re Looking For

• Strong interest in Machine Learning, NLP, and LLM applications
• Solid Python skills and familiarity with ML/NLP libraries (HuggingFace, Scikit-Learn, PyTorch/TensorFlow, LlamaIndex)
• Understanding of embeddings, tokenization, and vector search fundamentals
• Exposure to RAG workflows or frameworks like LangChain or LlamaIndex (personal or academic projects count)
• Curiosity to learn full-stack ML engineering from experimentation to deployment

Bonus points for:

• Experience with FastAPI or backend fundamentals
• Knowledge graphs or semantic search
• Docker or basic DevOps familiarity
• An “agentic builder” mindset: self-review, iterative improvement, and comfort with tools like GitHub Copilot or Antigravity

What You’ll Get

• Hands-on experience building production Agentic AI systems for enterprises
• Mentorship from engineers across ML, backend, and automation domains
• Ownership and the opportunity to contribute directly to live deployments
• A fast-paced learning environment designed for growth
• Strong career outcomes including potential full-time conversion based on performance