AI/ML Engineering Intern

About BTG

BTG is an early-stage technology company pioneering intelligent software solutions at the crossroads of commerce, artificial intelligence, and financial technology. We aim to create AI-driven products that enhance business discoverability, searchability, and actionable insights on contemporary AI platforms. Our development focuses on the advanced AI infrastructure enabling seamless, intelligent commerce experiences.

Role Overview

As an AI/ML Engineering Intern at BTG, you will collaborate with seasoned engineers to contribute to the AI frameworks powering our solutions. The internship offers hands-on involvement with modern AI tools including Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), vector databases, and the Model Context Protocol (MCP). A significant part of your work will involve designing sophisticated merchant knowledge systems to standardize, enrich, organize, and securely retrieve commercial data while interfacing AI models with external APIs and commerce platforms.

Key Responsibilities

• Develop AI applications leveraging contemporary Large Language Models.
• Implement AI agents and integrate Model Context Protocol (MCP) components.
• Create pipelines for Retrieval-Augmented Generation using embeddings and vector searches.
• Architect systems for data ingestion, normalization, enhancement, and intelligent retrieval of merchant information.
• Integrate AI-driven applications with internal services and third-party APIs.
• Build structured outputs, orchestrate tool-calling workflows, and manage agent systems.
• Assess, test, and optimize the performance of AI systems.
• Engage in architectural planning, peer code reviews, and Agile/Scrum development practices.
• Gain knowledge in modern AI engineering methodologies through mentorship and collaboration.

Required Qualifications

• Current enrollment in a Master’s program or recent graduation (within six months) in Computer Science, AI, Machine Learning, Data Science, Software Engineering, or a related technical discipline.
• Demonstrable experience in AI engineering via academic projects, research, internships, open-source involvement, or substantial personal initiatives.
• Proficiency in Python programming.
• Experience creating applications utilizing Large Language Models.
• Practical skills in developing AI agents, MCP integrations, or similar agent orchestration frameworks.
• Familiarity with Retrieval-Augmented Generation, embeddings, semantic search, and vector database technologies.
• Background in integrating RESTful APIs or third-party APIs with applications.
• Experience handling structured and unstructured data, including normalization, enrichment, transformation, and schema mapping.
• Strong analytical thinking, debugging capabilities, and problem-solving aptitude.
• Ability to work autonomously in a remote setting.

Preferred Qualifications

• Knowledge of LLM platforms such as Amazon Bedrock, OpenAI, Anthropic Claude, or Gemini.
• Experience with AI agent development frameworks like LangGraph, OpenAI Agents SDK, CrewAI, or AutoGen.
• Expertise in designing Model Context Protocol servers or building custom MCP tools.
• Background in pipeline construction for data ingestion, normalization, enrichment, ETL, or knowledge graph management.
• Experience with vector databases including MongoDB Atlas Vector Search, Pinecone, Weaviate, Qdrant, or Chroma.
• Proficiency in integrating AI with REST APIs, GraphQL, or external platforms.
• Familiarity with AWS cloud services and AI infrastructure.
• Understanding of MLOps, model deployment, evaluation, and observability.
• Experience in startup environments, research projects, or contributions to open-source communities is advantageous.

Internship Details and Benefits

• Remote internship based in the United States.
• Unpaid, educational internship eligible for college credit or practical training where applicable.
• Flexible work schedule supporting remote collaboration.
• Mentorship and technical guidance from experienced AI engineers.
• Participation in engineering code reviews and exposure to enterprise-level AI infrastructure and software engineering standards.

Application Process

Applicants should submit a resume, GitHub profile, and portfolio or examples showcasing AI/ML projects. Highlighting academic work, research, internships, open-source contributions, or personal projects relevant to AI engineering, LLMs, AI agents, APIs, MCP, or allied fields is critical. Candidates selected for interviews must be prepared to discuss personally designed or significantly contributed technical projects.