AI ML Engineer Intern – Winter / Spring 2027 (Master’s or PhD)

Akoncagua AI; Lakeland (FL); On-site; USD 28,000 - 41,000; Part time

Job summary

Akoncagua AI is seeking an AI Automation Engineer Intern to help design and deploy secure AI agents and automated workflows for accounting and enterprise finance. The role focuses on building LLM-based agent systems, document-intelligence pipelines, and end-to-end evaluation frameworks in a collaborative research environment.

You will work on integrating agents with accounting platforms, databases, and internal APIs, while developing robust tracing, observability, and cost controls for

Qualifications

• Currently enrolled in a Master's or PhD program in Computer Science, AI/ML, or a related technical field, or equivalent practical experience.

Responsibilities

• Build and improve LLM agent workflows for document intake, classification, and transaction coding.
• Develop document-intelligence pipelines for invoices, receipts, contracts, and financial statements.
• Implement retrieval over accounting policies, vendor records, and historical transactions.
• Write evaluation harnesses that measure extraction accuracy, classification quality, and end-to-end workflow outcomes.
• Add tracing and observability for prompts, tool calls, failures, latency, and cost.
• Integrate agents with accounting platforms, databases, and internal APIs.

Skills

Python

Data structures

Debugging

Testing

LLM apps

LangGraph

AutoGen

Semantic Kernel

OpenAI Agents SDK

RAG

Vector databases

Docker

Git

Education

Masters/PhD in CS/AI

Tools

LangGraph

AutoGen

Semantic Kernel

OpenAI Agents SDK

Job description

AI ML Engineer Intern – Winter / Spring 2027 (Master’s or PhD)

Akoncagua AI builds domain-specialized AI systems for technically demanding industries. Our work includes LLMs, multimodal AI, agentic automation, RAG, evaluation, and production AI infrastructure. We are seeking an AI Automation Engineer Intern to help build secure, reliable AI agents and automated workflows for accounting and enterprise finance.

What you’ll work on

• Build and improve LLM agent workflows for document intake, classification, and transaction coding
• Develop document-intelligence pipelines for invoices, receipts, contracts, and financial statements
• Implement retrieval over accounting policies, vendor records, and historical transactions
• Write evaluation harnesses that measure extraction accuracy, classification quality, and end-to-end workflow outcomes
• Add tracing and observability for prompts, tool calls, failures, latency, and cost
• Integrate agents with accounting platforms, databases, and internal APIs

Required qualifications

• Currently enrolled in a Master's or PhD program in Computer Science, AI/ML, or a related technical field, or equivalent practical experience
• Strong Python skills, plus solid fundamentals in data structures, debugging, and testing
• Experience building LLM-based applications, whether through coursework, research, personal projects, or internships
• Familiarity with at least one agent framework such as LangGraph, AutoGen/AG2, Semantic Kernel, or the OpenAI Agents SDK
• Working knowledge of RAG and vector databases
• Comfortable with Docker and version control in a collaborative codebase

Preferred qualifications

• Experience with MCP or similar tool-integration standards
• Experience with n8n, Zapier, Make, or event-driven automation
• Exposure to FastAPI, PostgreSQL, pgvector, or Kubernetes
• Interest in or exposure to accounting concepts such as double-entry bookkeeping, journal entries, reconciliations, or approval controls
• Awareness of prompt-injection risks, least-privilege design, and secrets management
• Strong understanding of multi-GPU Linux server environments, with hands-on experience configuring, managing, and effectively utilizing GPU resources.
• Open-source contributions or publications at AI conferences

How we work

We care about researchers and engineers who understand and can defend the design decisions in their code. AI coding tools are welcome here, but you should be able to explain why a system works the way it does, what evidence supports it, and where it breaks.

• Our work connects model research with the systems required to run it. You will have room to explore new ideas, but you will also be expected to build working implementations, measure them carefully, and communicate their limitations clearly.