We are looking for a mid-level or senior candidate for the role of AI Developer with experience in Python, LLM, and RAG for our partner on a project developing robust enterprise AI platforms and innovative agent workflows.
Development and advancement of an advanced platform foundation for reusable AI components and integration of complex, tailor-made solutions for enterprise clients
Collaboration in an agile, technically strong team of 3–5 people (architect, AIOps, developer, business) on the design of robust solutions without complex corporate structures
Integration of structured data with unstructured sources and solving complex use cases from prototype to production deployment with an emphasis on quality and cost monitoring
Design and implementation of components for RAG, context engineering, and orchestration of agent flows using Python, a model-agnostic approach with LiteLLM, Kubernetes, and modern AI assistants
Work performance in a hybrid collaboration mode with a ratio of 3 days onsite in Prague and 2 days remote
Requirements
Advanced experience with:
Development in Python and software engineering (at least 3 years for mid-level, at least 5 years for senior-level)
Design and implementation of complex RAG architectures (from chunking and embedding strategy to retrieval, reranking, and quality evaluation)
Experience with:
Working with LLMs and agentic frameworks, including orchestration of complex flows and agent memory management
Practical prompt engineering and context engineering (context structuring, prompt patterns)
Actively using AI coding assistants like Claude Code, Cursor, GitHub Copilot in daily practice
Working with vector databases and implementing hybrid search (BM25 + vector)
Knowledge of:
Czech and English at a working level for effective communication within the team and with clients
Basic principles of containerization and application deployment
Advantageous:
Experience with computer vision and working with embedding models for images or video
Knowledge of graph databases, GraphRAG, and knowledge graphs for enterprise data
Practical experience with fine-tuning (LoRA / QLoRA) and deployment of open-source models
Knowledge of guardrails, prompt injection mitigation, and AI safety practices
Familiarity with DevOps, Kubernetes, enterprise security, or on-premise deployments
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