🚀 About Code at Random
Code At Random is an early-stage startup building an AI-driven Edtech platform — that helps learners, students, and professionals upskill, find opportunities, and grow.
We’re building the web platform that powers this mission — with a strong focus on learning analytics, community interaction, and AI-driven personalization.
🧠 Role Overview
As an AI Engineer Intern , you’ll work closely with the founder and development team to design, train, and deploy the AI models behind our product.
You’ll handle everything from data collection → model experimentation → API deployment — in a fast-moving, startup-style environment.
⚙️ Key Responsibilities
Develop and fine-tune ML / NLP models for :
- Personalized learning recommendations
- Career & skill path prediction
- Resume / job matching
- Sentiment analysis of community discussions
- Work on data preprocessing, cleaning, and labeling pipelines
- Implement vector databases (e.g., Pinecone, ChromaDB, Weaviate) for embeddings
- Integrate LLM APIs (OpenAI, Anthropic, Gemini, Hugging Face) into backend services
- Experiment with RAG (Retrieval-Augmented Generation) and context retrieval systems
- Build and expose model endpoints as REST APIs or via FastAPI / Flask
- Collaborate with the full-stack developer to integrate AI into the web frontend
- Track model performance, accuracy, and improvement over time
- Maintain experiments and results documentation on Notion / ClickUp
🧩 Required Technical Skills
🧮 Core Machine Learning & NLP
Python (NumPy, pandas, scikit-learn)NLP libraries : Hugging Face Transformers, spaCy, NLTKVector embeddings : Sentence Transformers, OpenAI EmbeddingsModel evaluation & fine-tuning (classification, recommendation, similarity)⚙️ AI Systems & Deployment
Building APIs with FastAPI / FlaskFamiliar with Docker and containerizationKnowledge of Git / GitHub workflowsUnderstanding of cloud deployment (Azure / AWS / GCP / Vercel)🧠 LLM Integration & Prompt Engineering
Experience using OpenAI API, Gemini, Claude, or similar LLMsPrompt design and evaluation for dynamic user interactionsUnderstanding of RAG systems, embeddings, and context retrieval🗃️ Data & Storage
Familiarity with SQL / NoSQL databasesKnowledge of Vector DBs (Chroma, Pinecone, Weaviate)Basic understanding of data pipelines and preprocessing tools🌱 Good to Have
Experience with LangChain / LlamaIndex / HaystackUnderstanding of recommendation systemsExposure to knowledge graph / semantic search conceptsBasic experience in AI evaluation metrics and MLOps fundamentalsFamiliarity with Azure Cognitive Services or OpenAI on Azure🧩 Tools You’ll Work With
🧠 Hugging Face, OpenAI API, LangChain🧩 FastAPI, Flask, ChromaDB🧮 Python, scikit-learn, PyTorch, pandas💾 GitHub🎯 What You’ll Learn / Gain
Build end-to-end AI features that ship to real usersLearn RAG and LLM pipeline integration in productionWork on secure, cloud-based dev environmentsCollaborate directly with the founder and engineersGet exposure to both AI system design and product thinking🕒 Work Setup
Weekly goals + progress reviewsMust have good Wi-Fi connection [minimum 40Mbps]Kindly fill-up the Google form and submit your details : https : / / forms.gle / b3vAXhcBGazW9pco8