AI Engineer
Summary
AI Engineer at ProSafety, an embedded-systems development service provider, in Sarajevo (hybrid). Day to day: extend a RAG system toward AI agents, build document-ingestion/embedding/retrieval pipelines, and run AI infrastructure on OpenShift/Kubernetes, using Python, LangChain/LangGraph, FastAPI, Docker and vLLM.
- Master's or Bachelor's degree in Computer Science, Artificial Intelligence / Machine Learning, Mathematics, Physics, Embedded Systems Engineering, or a comparable technical field
- Professional experience in software development and strong proficiency in Python
- Solid knowledge of modern software engineering practices, including version control, testing, and containerization
- Practical experience or strong technical knowledge in at least one of the following areas: Large Language Models and RAG systems, agent frameworks such as LangChain or LangGraph, Kubernetes/OpenShift, or relational and vector databases
- Experience with document ingestion, embeddings, retrieval, or comparable AI/data pipelines is an advantage
- Knowledge of LLM serving technologies such as vLLM, GitOps tools such as ArgoCD, FastAPI, Docker, Prometheus/Grafana, or standards-driven development environments is an advantage
- Ability to work independently, structure complex technical problems, and communicate technical decisions clearly
- Communicative, proactive, and comfortable working across technical teams and business functions
- Languages: English is required, German is desirable
- Develop and extend our existing Retrieval-Augmented Generation (RAG) system and contribute to its evolution toward specialized AI agents and multi-agent systems
- Design and implement LLM-based workflows for concrete internal use cases, such as requirements extraction from technical documentation, knowledge management, employee onboarding, and research across technical standards
- Take AI use cases from initial concepts and prototypes to reliable, production-ready solutions
- Develop and maintain data pipelines for document ingestion, embedding generation, and retrieval
- Work with relational and vector databases and develop retrieval strategies, including hybrid retrieval
- Develop and integrate AI agents using modern agent frameworks and orchestration approaches
- Contribute to the operation and further development of our AI infrastructure on OpenShift and Kubernetes
- Apply modern software engineering practices, including version control, testing, containerization, and CI/CD
- Support deployment, monitoring, and observability of AI services and workflows
- Work closely with the AI team, engineering teams, and internal domain experts
- Contribute technical perspectives and ideas to the further development of our AI architecture and strategy
- Innovative and varied tasks in the development of tailor-made solutions for our customers
- An attractive workplace
- Convenient location as well as very good accessibility by public transport
- High degree of self-determination and short decision-making paths
- Collegial cooperation and flat hierarchies
- Internal and external training and further education opportunities
- Flexible working hours
