Senior AI Solutions Architect with LLM and RAG, Knowledge Graph Platform
- Almatý
- Astaná
- Belgrado
- Breslavia
- Cluj-Napoca
- Cracovia
- Dnipró
- Ereván
- Járkov
- Kyiv
- Lárnaca
- Leópolis
- Lodz
- Lublinie
- Odesa
- Remote.Bulgaria
- Remote.Georgia
- Remote.Kazakhstan
- Remote.Poland
- Riga
- Sofia
- Tiflis
- Varna
- Varsovia
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Project overview
Position overview
Technology stack
Responsibilities
- Design and manage end to end LLM orchestration and retrieval pipelines
- Define embedding model selection and chunking strategies, including context window management and trade offs affecting retrieval quality and cost
- Own the entity extraction pipeline to convert unstructured content into graph nodes and relationships
- Implement entity resolution, relationship normalization, and deduplication processes
- Design and refine semantic search strategies and retrieval logic across graph and vector layers
- Develop prompt engineering approaches and agentic workflows for advanced use cases
- Integrate graph based outputs with enterprise AI platforms such as Gemini
- Design and maintain evaluation frameworks including ground truth dataset creation
- Measure and improve retrieval quality using metrics such as recall, precision at K, faithfulness, and answer relevance
- Establish systematic regression testing practices for AI pipelines
- Optimize LLM usage costs across the full retrieval and generation lifecycle
- Implement observability, logging, and tracing to monitor performance and reliability
Requirements
- Experience designing and implementing LLM based systems in production environments
- Hands on experience with retrieval augmented generation and semantic search
- Strong understanding of embeddings, vector search, and chunking strategies
- Experience building entity extraction pipelines and working with knowledge graphs
- Proficiency in Python and data processing workflows
- Understanding of prompt engineering and agent workflow design
- Experience defining evaluation frameworks and quality metrics for AI systems
- Familiarity with distributed systems and scalable data architectures
- Experience implementing observability, logging, and tracing in data intensive environments
Nice to have
- Experience with Google Cloud Platform services including Cloud Spanner and Vertex AI
- Familiarity with enterprise AI platforms such as Gemini
- Knowledge of cost optimization techniques for large scale LLM systems
- Experience with graph data models and hybrid architectures combining graph, relational, and vector data
- Exposure to advanced evaluation techniques for generative AI and ranking systems
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