Artificial Intelligence Engineer Intern

About Us

We are an AI-focused technology company, building conversational AI, AI agents, and automation solutions for healthcare, education, and enterprise and mid-market customer support.

Role Overview

We are moving from traditional AI to advanced, LLM-powered systems, and we want to do it cost-effectively. As an AI Engineer Intern, you will help build AI agents, conversational AI, and automation for real client use cases, working closely with our senior engineering team. You will learn to choose the right approach for each problem, whether that is an LLM, a smaller open-source model, or classic machine learning, balancing quality, speed, and cost.

Key Responsibilities

AI Agents and LLM Applications

• Build AI agents that use tools, call APIs, and complete multi-step tasks
• Develop conversational AI for chat, WhatsApp, and voice channels
• Build retrieval-augmented generation (RAG) systems over client documents and data
• Write, test, and refine prompts and agent workflows

Cost-Effective AI

• Compare commercial APIs with open-source models for quality, speed, and cost
• Run and serve open-source models efficiently using quantization and optimized inference
• Reduce costs through caching, model routing, and smaller task-specific models
• Track token usage, latency, and cost per conversation

Models and Data

• Fine-tune small and medium models for specific tasks where it beats prompting
• Apply classic machine learning where it fits better than LLMs, such as classification, prediction, and lead scoring
• Prepare, clean, and manage datasets from sources such as CSV, SQL, and cloud storage

Evaluation and Deployment

• Build evaluation sets and measure accuracy, hallucinations, and response quality
• Run experiments and A/B tests to compare approaches
• Package and deploy AI services as APIs with the engineering team
• Monitor live systems and help improve them over time

Research

• Keep up with new models, agent frameworks, and techniques
• Test promising tools and share practical findings with the team

Requirements & Tech Stack

Programming

• Python: strong fundamentals, clean and readable code
• Git: version control and collaboration
• REST APIs and JSON: building and calling APIs

AI and LLMs

• LLM APIs: hands-on use of OpenAI, Anthropic Claude, or Google Gemini
• Prompt engineering: structured outputs, function or tool calling
• RAG basics: embeddings, chunking, vector databases such as Chroma, Qdrant, or pgvector
• Agent frameworks: LangChain, LangGraph, LlamaIndex, or similar
• Hugging Face: using and running open-source models such as Llama, Mistral, or Qwen

Machine Learning Fundamentals

• Core ML concepts: supervised learning, evaluation metrics, overfitting
• PyTorch or scikit-learn: basic hands-on use
• Data handling: pandas, NumPy, and SQL

Good to Have

• Running local or self-hosted models with Ollama or vLLM
• Fine-tuning with LoRA or QLoRA
• Voice AI: speech-to-text and text-to-speech tools such as Whisper
• Model Context Protocol (MCP) or other ways of connecting agents to tools
• LLM evaluation and monitoring tools such as Langfuse, LangSmith, or Ragas
• FastAPI, Docker, and a cloud platform such as AWS, GCP, or Azure
• Interest in healthcare, education, or customer support use cases

Qualifications

• BE, BTech, ME, MTech, or MCA in Computer Science, AI, Data Science, or a related field, completed or in final year
• Projects, a GitHub profile, hackathons, or internships showing hands-on AI work; please share links

What We Offer

• Hands-on work on live AI agents and products used by real clients
• Mentorship from senior engineers
• Exposure to the full cycle: idea, build, evaluation, and deployment
• Opportunity for a full-time role based on performance

What We Are Looking For

• Builder: ships working prototypes, not just notebooks
• Cost-aware: thinks about efficiency as well as accuracy
• Curious: experiments with new models and tools
• Research-minded: tests ideas and measures results
• Self-driven: learns quickly and works well with ambiguity