Part-Time AI Engineer (LLMs) – Remote, Europe (German & English)

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Start: 11/2025 | Remote: 100%

Beschreibung

Posted by
InnovTM
Contact person
Daniel Gonzalez
Project ID
2945046
Automation
Microsoft Azure
Quality Auditing
Databases
Continuous Integration
Infrastructure Management
Package Management Systems
Production Systems
Regression Testing
Workflows
Data/Record Logging
Metrics
Data Science
Flask (Web Framework)
Large Language Models
Tooling Assembly and Dismantling
Data Protection
Fastapi
GDPR
React Redux
Docker
Microservices
Description
About Us
We are an early-stage startup building AI-powered tools for European SMEs, with a focus on customers in the DACH region (Germany, Austria, Switzerland).
Our product helps teams automate document-heavy workflows (search, summarization, Q&A, internal assistants) using modern Large Language Models (LLMs).
We’re looking for a part-time AI Engineer to join us on a very flexible basis and help us design and ship our next generation of LLM features.

Role Details
Job type: Part-time (independent contractor or freelancer)
Location: Fully remote, based in the EU/EEA or Switzerland (CET ±2 hours)
Hours: Up to 10 hours per week (usually 5–8 hrs/week)
Compensation:$50 USD per hour
Duration: Initial 3-month engagement, with strong potential to extend if we’re a good mutual fit

What You’ll Do
Design and implement LLM-based features:
Chat-style assistants, document Q&A, summarization, internal search, etc.
Build and improve RAG (Retrieval-Augmented Generation) pipelines:
Chunking, embeddings, vector search, ranking, caching.
Integrate with major AI providers (OpenAI, Anthropic, Azure OpenAI, etc.) and tooling (LangChain, LlamaIndex or similar).
Develop small Python microservices/APIs to connect AI features to our existing backend (Node.js/TypeScript).
Set up lightweight evaluation & monitoring:
Prompt regression tests, quality checks, simple guardrails and fallbacks.
Work closely with the founders to translate German-language customer requirements into technical solutions, while collaborating internally in English.
Document your work clearly so the rest of the team can extend and maintain it.

Requirements
3+ years of professional experience in ML / AI / Data Science / ML Engineering, including production systems.
Strong Python skills (experience with FastAPI/Flask or similar is a plus).
Hands-on experience building LLM-powered applications, including:
Calling LLM APIs (OpenAI / Anthropic / Azure OpenAI / similar)
Using at least one orchestration library (LangChain, LlamaIndex, custom tooling, etc.)
Working with vector databases (Pinecone, Weaviate, Qdrant, pgvector, etc.)
Good understanding of:
Prompt design and evaluation
RAG architectures
Basic MLOps (environments, package management, simple CI/CD)
Languages
German: B2+ or native – you can comfortably read and understand German business documents and user content
English: B2+ or higher – for daily communication and documentation
Comfortable working very independently in a low-hour setting (<10 hours/week) with async communication (Git, issues/PRs, short status calls).

Nice-to-Haves
Experience in B2B SaaS or products for SMEs in the DACH market.
Familiarity with GDPR / EU data privacy applied to AI/ML.
Experience with:
Docker & containerized deployments
A major cloud provider (AWS, GCP, or Azure)
Observability tools (logging, metrics, tracing, error tracking)
Previous experience as a freelancer / consultant on part-time or hourly engagements.

What We Offer
Fully remote, flexible work from anywhere in the EU/EEA or Switzerland.
Very flexible schedule – we only need 1–2 short calls per week overlapping with CET.
A chance to work directly with the founders and influence our AI roadmap.
Well-scoped tasks suitable for limited hours (no expectation of overtime or “hidden full-time” work).
Potential to increase hours or move toward a larger role as we grow.

How to Apply
Please include:
A short introduction (2–3 paragraphs) about your AI/LLM experience and current weekly availability.
Your CV or LinkedIn profile, plus any GitHub / portfolio / demo links.
A brief description of one LLM-based system you’ve built:
What problem it solved
Your role
Tech stack and infrastructure
We review applications on a rolling basis and will contact shortlisted candidates for a short video call.