AI Engineer – RAG and Agentic AI (m/f/d)

Responsibilities

• Design, implement, test, and continuously optimize end-to-end RAG pipelines, including data parsing, ingestion, prompt engineering, and chunking strategies.
• Curate and develop high-quality datasets, using synthetic data generation for robust training and evaluation.
• Rigorously evaluate LLM applications on metrics including correctness, latency, and hallucination.
• Assist in the deployment of LLM-based applications, analyze user feedback, and contribute to iterative improvements.
• Write clean, maintainable, and testable code following best practices.
• Collaborate with cross-functional teams to integrate AI components into other systems.

Requirements

• Master's or Ph.D. in Computer Science, Machine Learning, or a related field and a minimum of 2 years of hands-on industry experience in software engineering.
• Experience operating RAG systems in production environments, including monitoring, debugging, and continuous improvement based on real user behavior.
• Solid understanding of software engineering practices applied to AI systems (testing, CI/CD integration, versioning, and reproducibility).
• Ability to balance research innovation with long‑term maintainability and customer‑ready quality standards.
• Clear communication and presentation skills.

Good To Have

• Experience with observability stacks (e.g., Prometheus, Grafana, OpenTelemetry) applied to AI or backend services.
• Familiarity with enterprise deployment constraints such as air‑gapped systems, license compliance, and distribution of AI‑enabled software to customers.
• Exposure to agent frameworks, tool‑calling patterns, or multi‑step reasoning architectures.
• Hands‑on experience with vector databases (e.g., Milvus) and modern RAG architectures, such as Graph‑based Retrieval‑Augmented Generation.

This role emphasizes long‑term ownership of Retrieval‑Augmented Generation systems as a core product capability, not just experimentation with large language models.