Data Science / ML Engineer Intern

Job Description

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