Python Developer Intern (AI / ML)

Job Summary

Internship Structure Duration: 3 months, on-site.

Conversion: Performance during the internship is reviewed at the end of the term. Strong performers are converted to a full-time role at Arcitech AI; this is not automatic and is assessed against the same bar as our full-time hires for this function.

Prior project experience (academic, personal, freelance, hackathon, or open-source) directly relevant to this role is required to be considered - we are not hiring purely on academic credentials with no applied work to show.

Role Overview

Arcitech AI is looking for a Python AI/ML Developer Intern to work alongside our AI/ML engineering team on real LLM-powered features - think RAG pipelines, prompt-driven workflows, and integrations with LLM APIs - not isolated notebook exercises. Strong performers over the 3-month term will be considered for a full-time Python Developer (AI/ML) role.

This is a specialized track: we expect you to already have hands-on exposure to at least one ML/LLM project - a model you trained, a RAG pipeline you built, or an LLM API you integrated into something real. We will deepen your depth in our stack (LangChain, vector databases, FastAPI); we are not starting from AI/ML fundamentals.

Responsibilities

• Assist in building LLM-powered workflows and RAG pipelines using LangChain, under the guidance of a senior AI/ML engineer.
• Work with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and help implement and test prompt strategies.
• Get hands-on with embedding models and vector search - help implement or maintain similarity-search components (pgvector, FAISS, or similar).
• Support model experimentation using PyTorch, TensorFlow, or Hugging Face Transformers on assigned tasks.
• Build and maintain small Python microservices using FastAPI to expose AI features.
• Write tests for AI service code (pytest) and follow our Git and code-review workflow.
• Own one small, well-scoped AI feature or pipeline component end-to-end during the internship, as a basis for your conversion evaluation.

Qualifications

• Pursuing or completed a Bachelors or Masters degree in Computer Science, AI/ML, or a related field.
• 0-1 years of professional experience - internships, coursework, and self-driven projects all count.
• Mandatory: at least one AI/ML or LLM project - a fine-tuned or trained model, a RAG pipeline, an LLM- API-integrated application, or a similar applied project. Coursework alone without an applied build does not qualify. Applications without a demonstrable project will not be considered.

Skills

• Working knowledge of Python.
• Some hands-on exposure to PyTorch, TensorFlow, or Hugging Face Transformers.
• Basic understanding of how LLMs and embeddings work; bonus if you've used an LLM API (OpenAI, Anthropic, etc.) in a project.
• Some exposure to LangChain, or a clear ability to learn it quickly.
• Basic understanding of REST APIs; some exposure to FastAPI or Flask is a plus.
• Strong analytical thinking and comfort with ambiguity - AI/ML work often doesn't have a single right answer.
• Clear communication - able to explain your model/pipeline choices and trade-offs.

Nice to Have

• Exposure to a vector database (pgvector, Pinecone, FAISS, Weaviate, Milvus).
• Any experience with prompt engineering or evaluating LLM outputs.
• Exposure to Docker.
• Kaggle competitions, published notebooks, or open-source ML contributions.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.