Senior AI & Data Engineer — Remote/Hamburg

Schickler; United States; Remote; USD 100,000 - 133,000; Full time

Job summary

Schickler seeks a senior data and AI engineer to drive data/AI initiatives, based in Hamburg or remote, with permanent full-time status. The role targets an experienced practitioner at the intersection of data engineering, ML, and applied AI, supporting projects from data architecture to modern language-model-based systems.

Responsibilities include designing data pipelines, cloud architecture, and deploying ML workflows, including RAG and NLP-based systems, within an IT-focused team.

Qualifications

• Multiple years of professional experience as Data Engineer or AI Engineer.
• Proficiency in Python and modern data engineering tooling.
• Hands-on experience with at least one major cloud platform (AWS/Azure/GCP).
• Strong understanding of data warehousing, ETL pipelines, and data architecture.
• Experience with ML workflows and applied AI.
• Familiarity with LLMs, RAG, NLP, and agent-based approaches.

Responsibilities

• Design, build, and maintain data pipelines, ETL workflows, and data warehouse components.
• Contribute to data architecture decisions across cloud environments.
• Develop and deploy machine learning and AI engineering solutions, including LLM- and NLP-based systems.
• Explore and implement retrieval-augmented generation (RAG) and agent-based patterns.
• Collaborate on project management and software delivery within an IT-focused team.

Skills

Python

Data engineering

AI engineering

Data pipelines

Cloud platforms

LLMs

RAG

NLP

ETL pipelines

Data warehousing

Tools

AWS

Azure

GCP

Job description

Schickler seeks a senior data and AI engineer to drive data/AI initiatives, based in Hamburg or remote, with permanent full-time status. The role targets an experienced practitioner at the intersection of data engineering, ML, and applied AI, supporting projects from data architecture to modern language-model-based systems.

Responsibilities include designing data pipelines, cloud architecture, and deploying ML workflows, including RAG and NLP-based systems, within an IT-focused team.