AI Automation & Workflow Engineering Intern

Atomity; Auerbach; Teletrabalho; EUR 16 000 - 22 000; Tempo integral

Resumo da oferta

Atomity is seeking an AI Automation & Workflow Engineering Intern to help automate internal processes across the company. You will map manual tasks into triggers, data flows and failure paths, then build reliable automations using APIs and low-code tools.

You will work with Python and TypeScript, n8n, LangGraph, LangChain and other services to implement deterministic logic with AI where valuable. This is a remote internship with close collaboration with the founders and teams across

Qualificações

• Currently pursuing or recently completed a degree in Computer Science, Software Engineering, Information Systems, AI, Automation or a related field.
• Strong logical and problem-solving skills.
• Understanding of APIs, JSON and webhooks.
• Ability to break an operational process into explicit steps and edge cases.
• Interest in workflow automation and AI agents.

Responsabilidades

• Map manual processes into clear triggers, steps, data flows, decisions and failure paths.
• Build workflow automations for recurring and ad-hoc internal tasks.
• Connect SaaS products, internal services and self-hosted systems through APIs, webhooks and event-driven workflows.
• Use LLMs for classification, extraction, summarisation, routing and structured data generation.
• Design reusable components, prompts, connectors and templates for automation reuse.

Conhecimentos

Python

TypeScript

APIs

LLM integration

Workflow automation

n8n/Make

Docker

PostgreSQL

Observability

Git

Formação académica

Bachelor's degree in Computer Science

Ferramentas

n8n

LangGraph

LangChain

FastAPI

Next.js

Descrição da oferta de emprego

AI Automation & Workflow Engineering Intern

This is a full-time, remote internship role.

As an AI Automation & Workflow Engineering Intern, you will help Atomity automate internal work across the company. The automation needs can come from engineering, recruiting, sales, research, finance, operations or entirely new processes that do not exist yet.

Your job is not to automate one fixed set of tools. You will identify repetitive processes, understand their triggers and decision points, and choose the right implementation: a deterministic workflow, an API integration, an AI-assisted pipeline, an agentic workflow or a small custom internal tool. The goal is reliable automation that saves time without creating hidden operational risk.

• Map manual processes into clear triggers, steps, data flows, decisions, approvals and failure paths.
• Build workflow automations for recurring and ad-hoc internal tasks.
• Connect SaaS products, internal services and self-hosted systems through APIs, webhooks and event-driven workflows.
• Use LLMs for tasks such as classification, extraction, summarisation, routing, drafting and structured data generation where they add real value.
• Use deterministic logic where reliability and auditability are more important than model flexibility.
• Build agentic workflows for multi-step tasks that require tool use, context or dynamic decision-making.
• Add human-in-the-loop approval for sensitive or high-impact actions.
• Build custom Python or TypeScript services when a low-code workflow is not the right solution.
• Create lightweight internal tools or interfaces when employees need a reusable self-service workflow instead of a background automation.
• Work with document ingestion, parsing, OCR and structured extraction where processes begin with PDFs, forms or other unstructured files.
• Design reusable components, prompts, connectors and templates instead of rebuilding the same automation repeatedly.
• Implement retries, idempotency, logging, monitoring and alerting so automations fail safely and can be debugged.
• Handle credentials, secrets and permissions securely.
• Evaluate new automation and agent frameworks and decide whether to use, extend or replace them.
• Prototype quickly, measure time saved and operational reliability, then productionise the workflows that prove useful.
• Document workflows clearly so they can be maintained by the team after handover.
• Workflow orchestration tools such as n8n, Activepieces or Make
• Agent frameworks such as LangGraph, LangChain or similar
• Python and TypeScript
• REST and GraphQL APIs
• Webhooks and event-driven integrations
• LLM APIs and open-source models
• Tool calling and structured outputs
• MCP and other emerging tool-integration standards where useful
• OCR and document-parsing pipelines
• Docker and self-hosted services
• PostgreSQL / Redis or similar state stores
• Schedulers, queues and background jobs
• Lightweight internal applications using frameworks such as FastAPI, Next.js or Streamlit
• Currently pursuing or recently completed a degree in Computer Science, Software Engineering, Information Systems, AI, Automation or a related field.
• Strong logical and problem-solving skills.
• Understanding of APIs, JSON and webhooks.
• Ability to break an operational process into explicit steps and edge cases.
• Interest in workflow automation and AI agents.
• Ability to test workflows systematically rather than assuming they work.
• Good written communication and documentation skills.
• Comfortable working on changing priorities in an early-stage company.
• Hands-on experience with n8n or another workflow automation platform.
• Experience with LangGraph, LangChain, PydanticAI or another agent framework.
• Experience building API integrations.
• Knowledge of queues, schedulers, retries and asynchronous jobs.
• Experience with OCR, document extraction or structured-output pipelines.
• Experience building small internal tools or dashboards.
• Understanding of human-in-the-loop AI patterns.
• Experience self-hosting automation or AI tools.
• Familiarity with observability, logging and workflow testing.
• Experience with Docker.
• Interest in designing systems that combine deterministic automation with AI only where needed.
• How to decide whether a business process needs a workflow, an AI step, an agent or custom software.
• How reliable production automations are designed around failures, retries and approvals.
• How LLMs can be safely combined with deterministic business logic.
• How agentic systems use tools, state, memory and human oversight.
• How to integrate unrelated systems through APIs and events.
• How to turn ad-hoc operational work into reusable internal systems.
• How to build small internal tools when no existing product fits the workflow.
• How to measure whether an automation actually saves time and improves operations.
• How automation architecture evolves as an early-stage company grows.
• Work on automation problems across the whole company rather than one narrow function.
• Build systems that employees use in real day-to-day operations.
• Experiment with modern workflow and agent technologies while staying focused on reliability.
• Work directly with the founders and different functional teams.
• See the full lifecycle from process discovery to prototype to production automation.
• High ownership from day one.
• Opportunity to grow into an automation, AI engineering or internal-platform role as Atomity scales.

Technical Issues?
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