The AI Signal

AI Engineer Internship

Location not stated · Remote

This internship is unpaid.
Apply by 27 Nov 2026
Verified open 4 hours ago

The employer’s own application page isn’t available on Raaha for this listing. Search for the role on the company’s careers page and confirm the stipend, deadline and eligibility there before you apply.

Key facts

Company: The AI Signal
Role: AI Engineer Internship
Location: Location not stated
Work mode: Remote
Hours: Part-time
Stipend: This internship is unpaid.
Duration: Not stated
Start date: Not stated
Apply by: 27 Nov 2026
Openings: Not stated
Posted: 28 Oct 2026
Field: AI / ML

Our summary

AI Engineer Internship at The AI Signal.

Raaha’s short summary of the listing, not the employer’s full description. Read the employer’s page for their exact wording.

Responsibilities

Build AI-powered pipelines for data acquisition, processing, enrichment, and automation.
Integrate and experiment with LLMs from providers such as OpenAI, Anthropic, Google, and OpenRouter.
Develop workflows for structured extraction, classification, summarization, tagging, and information synthesis.
Build scalable web crawlers and data ingestion pipelines to collect information from websites, APIs, documentation, GitHub, and other public sources.
Develop systems to discover, organize, and enrich information about AI models, tools, APIs, datasets, research papers, benchmarks, and emerging technologies.
Implement embedding generation, semantic search, vector retrieval, and Retrieval-Augmented Generation (RAG).
Build and maintain data processing pipelines, validation rules, deduplication, and entity resolution.
Experiment with model selection, prompt engineering, structured outputs, and tool calling.
Design evaluation pipelines to measure model accuracy, relevance, latency, reliability, and cost.
Implement robust error handling, retries, rate-limit management, caching, and fallback strategies for AI API integrations.

All internships

Eligibility

Engineering Students

Postgraduate

Undergraduate

Fresher

Arts, Commerce, Sciences & Others

Strong proficiency in Python and solid programming fundamentals.

Understanding of APIs, JSON, HTTP, asynchronous programming, and backend development.

Hands-on experience building at least one meaningful project using an LLM API or open-source language model.

Understanding of LLM fundamentals, prompt engineering, tokens, context windows, and structured outputs.

Familiarity with data processing, web scraping, and data ingestion using tools such as Requests, BeautifulSoup, Playwright, or Scrapy.

Experience with PostgreSQL, SQLite, MongoDB, or another database.

Familiarity with Git and GitHub.

Understanding of data structures, algorithms, debugging, and writing maintainable code.

Ability to evaluate technical approaches through experiments rather than relying solely on model outputs or assumptions.

Strong problem-solving skills and the ability to work independently in a fast-paced startup environment.

A portfolio of AI projects, GitHub repositories, research implementations, or working demos that demonstrate practical engineering ability.

Good to Have

Experience with OpenRouter, OpenAI, Anthropic, Gemini, or other LLM APIs.

Familiarity with LangChain, LlamaIndex, Hugging Face Transformers, or agent frameworks.

Experience with embeddings, vector databases such as Qdrant, pgvector, or Pinecone, and RAG pipelines.

Knowledge of model evaluation, LLM observability, tracing, and experiment tracking.

Familiarity with MCP, function calling, multi-agent systems, or tool-use workflows.

Experience with Docker, cloud deployment, Redis, task queues, or workflow orchestration.

Understanding of model routing, provider fallbacks, latency optimization, token usage, and inference cost optimization.

Experience with PyTorch, fine-tuning, open-source models, or machine learning fundamentals.

Who Should Apply?

We want engineers who enjoy building and experimenting with real AI systems, not just creating chatbot demos. You should be comfortable reading documentation, testing multiple approaches, measuring results, and turning promising experiments into reliable software.

Strong Python fundamentals, practical project experience, and the ability to explain your technical decisions are more important than having used every framework listed above.