Avatar for Atomity
Atomity
Actively Hiring
ATOMITY is a multi-cloud orchestrator that routes enterprise workloads dynamically
  • Responds within three weeks
    Based on past data, Atomity usually responds to incoming applications within three weeks
  • Growing fast
    Showed strong hiring growth in the past month

AI Automation & Workflow Engineering Intern

  • No equity
  • |Remote (
    Everywhere
    )
  • |No experience required
  • |Internship
Posted: 7 days ago• Recruiter recently active
Hires remotely in
Everywhere
Remote Work Policy

Remote only

Company Location
Visa Sponsorship

Not Available

Preferred Timezones
Central European Time
RelocationAllowed
Skills
Automation
AI
Process Improvement
Data Processing
Generative AI

About the job

Design and build AI-assisted workflows, agents and lightweight internal tools that automate repetitive work across Atomity as new needs emerge.

About the Role

This is a full-time, remote internship role, Unpaid.

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.

What You Will Work On

  1. Map manual processes into clear triggers, steps, data flows, decisions, approvals and failure paths.
  2. Build workflow automations for recurring and ad-hoc internal tasks.
  3. Connect SaaS products, internal services and self-hosted systems through APIs, webhooks and event-driven workflows.
  4. Use LLMs for tasks such as classification, extraction, summarisation, routing, drafting and structured data generation where they add real value.
  5. Use deterministic logic where reliability and auditability are more important than model flexibility.
  6. Build agentic workflows for multi-step tasks that require tool use, context or dynamic decision-making.
  7. Add human-in-the-loop approval for sensitive or high-impact actions.
  8. Build custom Python or TypeScript services when a low-code workflow is not the right solution.
  9. Create lightweight internal tools or interfaces when employees need a reusable self-service workflow instead of a background automation.
  10. Work with document ingestion, parsing, OCR and structured extraction where processes begin with PDFs, forms or other unstructured files.
  11. Design reusable components, prompts, connectors and templates instead of rebuilding the same automation repeatedly.
  12. Implement retries, idempotency, logging, monitoring and alerting so automations fail safely and can be debugged.
  13. Handle credentials, secrets and permissions securely.
  14. Evaluate new automation and agent frameworks and decide whether to use, extend or replace them.
  15. Prototype quickly, measure time saved and operational reliability, then productionise the workflows that prove useful.
  16. Document workflows clearly so they can be maintained by the team after handover.

Tools & Approaches

  1. Workflow orchestration tools such as n8n, Activepieces or Make
  2. Agent frameworks such as LangGraph, LangChain or similar
  3. Python and TypeScript
  4. REST and GraphQL APIs
  5. Webhooks and event-driven integrations
  6. LLM APIs and open-source models
  7. Tool calling and structured outputs
  8. MCP and other emerging tool-integration standards where useful
  9. Browser automation where appropriate and permitted
  10. OCR and document-parsing pipelines
  11. Docker and self-hosted services
  12. PostgreSQL / Redis or similar state stores
  13. Schedulers, queues and background jobs
  14. Lightweight internal applications using frameworks such as FastAPI, Next.js or Streamlit

Requirements

  1. Currently pursuing or recently completed a degree in Computer Science, Software Engineering, Information Systems, AI, Automation or a related field.
  2. Strong logical and problem-solving skills.
  3. Basic programming ability in Python, JavaScript or TypeScript.
  4. Understanding of APIs, JSON and webhooks.
  5. Ability to break an operational process into explicit steps and edge cases.
  6. Interest in workflow automation and AI agents.
  7. Ability to test workflows systematically rather than assuming they work.
  8. Comfortable learning new tools quickly.
  9. Good written communication and documentation skills.
  10. Comfortable working on changing priorities in an early-stage company.

Nice to Have

  1. Hands-on experience with n8n or another workflow automation platform.
  2. Experience with LangGraph, LangChain, PydanticAI or another agent framework.
  3. Experience building API integrations.
  4. Experience with browser automation or scraping for legitimate internal workflows.
  5. Knowledge of queues, schedulers, retries and asynchronous jobs.
  6. Experience with OCR, document extraction or structured-output pipelines.
  7. Experience building small internal tools or dashboards.
  8. Understanding of human-in-the-loop AI patterns.
  9. Experience self-hosting automation or AI tools.
  10. Familiarity with observability, logging and workflow testing.
  11. Experience with Docker.
  12. Interest in designing systems that combine deterministic automation with AI only where needed.

Details

  1. Duration: 3-6 months
  2. Location: Remote only
  3. Start date: Flexible
  4. Salary: Unpaid

What You Will Learn

  1. How to decide whether a business process needs a workflow, an AI step, an agent or custom software.
  2. How reliable production automations are designed around failures, retries and approvals
  3. How LLMs can be safely combined with deterministic business logic.
  4. How agentic systems use tools, state, memory and human oversight.
  5. How to integrate unrelated systems through APIs and events.
  6. How to turn ad-hoc operational work into reusable internal systems.
  7. How to build small internal tools when no existing product fits the workflow.
  8. How to measure whether an automation actually saves time and improves operations.
  9. How automation architecture evolves as an early-stage company grows.

Why Join Atomity?

  1. Work on automation problems across the whole company rather than one narrow function.
  2. Build systems that employees use in real day-to-day operations.
  3. Experiment with modern workflow and agent technologies while staying focused on reliability.
  4. Work directly with the founders and different functional teams.
  5. See the full lifecycle from process discovery to prototype to production automation.
  6. High ownership from day one.
  7. Opportunity to grow into an automation, AI engineering or internal-platform role as Atomity scales.

About the company

Atomity company logo

Atomity

Actively Hiring
More jobs
ATOMITY is a multi-cloud orchestrator that routes enterprise workloads dynamically11-50 Employees
  • Responds within three weeks
    Based on past data, Atomity usually responds to incoming applications within three weeks
  • Growing fast
    Showed strong hiring growth in the past month
Learn more about Atomity image

Founders

Atomity HR
Founder
Bensheim
image
View the team image