Artificial Intelligence Engineer

0 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

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On-site

Job Type

Internship

Job Description

🚀 About Code at Random

AI-driven Edtech platform

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

AI Engineer Intern

data collection → model experimentation → API deployment


⚙️ Key Responsibilities


ML/NLP models


  1. Personalized learning recommendations
  2. Career & skill path prediction
  3. Resume/job matching
  4. 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, NLTK

  • Vector embeddings:

    Sentence Transformers, OpenAI Embeddings

  • Model evaluation & fine-tuning (classification, recommendation, similarity)


⚙️ AI Systems & Deployment

  • Building APIs with

    FastAPI / Flask

  • Familiar with

    Docker

    and containerization
  • Knowledge of

    Git / GitHub workflows

  • Understanding of

    cloud deployment (Azure / AWS / GCP / Vercel)


🧠 LLM Integration & Prompt Engineering

  • Experience using

    OpenAI API, Gemini, Claude, or similar LLMs

  • Prompt design and evaluation for dynamic user interactions
  • Understanding of

    RAG systems, embeddings, and context retrieval


🗃️ Data & Storage

  • Familiarity with

    SQL / NoSQL databases

  • Knowledge of

    Vector DBs (Chroma, Pinecone, Weaviate)

  • Basic understanding of

    data pipelines

    and preprocessing tools


🌱 Good to Have

  • Experience with

    LangChain / LlamaIndex / Haystack

  • Understanding of

    recommendation systems

  • Exposure to

    knowledge graph / semantic search

    concepts
  • Basic experience in

    AI evaluation metrics and MLOps fundamentals

  • Familiarity 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 users

  • Learn

    RAG and LLM pipeline integration

    in production
  • Work on secure, cloud-based dev environments
  • Collaborate directly with the founder and engineers
  • Get exposure to both

    AI system design and product thinking


🕒 Work Setup

  • Weekly goals + progress reviews
  • Must have good Wi-Fi connection [minimum 40Mbps]


Email your updated Resume/CV along with your portfolio link (optional) atcodeatrandom@gmail.com

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