Responsibilities
- Design, develop, and maintain LLM orchestration frameworks that integrate static analysis tools, retrieval-augmented generation (RAG) systems, and automated testing pipelines
- Develop data ingestion and transformation pipelines to process source code, documentation, and test outputs for AI/ML workflows
- Build and maintain database solutions supporting RAG architectures, including vector stores, metadata management, and audit logging systems
- Collaborate with systems and software engineers to design and deploy AI-enabled tools in secure and distributed environments
- Conduct performance profiling, optimization, and scalability analysis of AI systems
- Support software engineering best practices across the development lifecycle, including testing, deployment, and documentation
Basic qualifications
- Strong proficiency in Python and experience integrating LLM-based systems into production workflows
- Experience with data engineering pipelines, including transformation and vectorization of structured and unstructured data
- Familiarity with static analysis tools and RAG-based architectures
- Experience working with relational and vector databases
- Understanding of Agile development practices and tools such as Git, Jira, and Confluence
Preferred qualifications
- Experience with Rust or systems-level programming languages
- Familiarity with AI development tools such as Claude Code or Codex
- Experience implementing CI/CD pipelines for AI/ML systems
- Experience with distributed systems or large-scale AI infrastructure
About the company
Our success is driven by the diverse skills, experiences, and perspectives each person brings to the team. We foster a collaborative culture built on teamwork, innovation, and shared goals — where ideas are valued, contributions are recognized, and everyone has the opportunity to make an impact.
When you join Tiber, you become part of a team that values excellence, continuous improvement, and the people behind the mission.