Artificial Intelligence Engineer
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Artificial Intelligence Engineer
Anzeige auf Englisch
Gehalt65.000–95.000 € brutto/Jahr
HomeofficeVollständig remote
- Develop and deploy ML and AI solutions for real-world business requirements.
- Build LLM applications using OpenAI, Azure OpenAI, Anthropic Claude, or similar models.
- Develop RAG, embeddings, vector search, and re-ranking solutions.
- Build AI agents and agentic workflows with appropriate guardrails and evaluation.
- Develop AI/ML pipelines and production-ready services using Python.
- Use AI coding tools such as Claude Code and Cursor alongside traditional coding.
- Implement MLOps, CI/CD, monitoring, logging, and evaluation practices.
- Deploy AI applications on Microsoft Azure using Docker and Kubernetes/AKS.
- Manage source code using Git, branching, and pull-request workflows.
- Work with product and business teams to understand requirements and deliver effective AI solutions.
- Ensure AI systems are secure, reliable, observable, and scalable.
- Participate in technical and code reviews and support other engineers.
Required Skills & Qualifications
- Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Mathematics, Statistics, or a related field.
- 4+ years of professional software engineering experience, including at least 2 years in production AI/ML.
- Experience with PyTorch, TensorFlow, scikit-learn, pandas, and NumPy.
- Hands-on experience with LLMs, RAG, embeddings, and vector databases.
- Experience with OpenAI, Azure OpenAI, Anthropic Claude, or open-source models.
- Experience with Claude Code, Cursor, or similar AI development tools.
- Strong knowledge of Git and code review practices.
- Experience with CI/CD, Azure DevOps, or GitHub Actions.
- Hands-on experience with Microsoft Azure, Docker, and Kubernetes/AKS.
- Strong communication and problem-solving skills.
- Ability to work effectively in a fully remote, international team.
Preferred Experience
- Agentic AI and AI orchestration.
- Prompt engineering and model evaluation.
- Fine-tuning and structured outputs.
- Terraform or Bicep.
- AI observability, monitoring, and MLOps.
- Experience with security, privacy, and responsible AI practices.
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