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Senior AI Solutions Architect with LLM and RAG, Knowledge Graph Platform

  • Almaty
  • Astana
  • Belgrade
  • Cluj-Napoca
  • Dnipro
  • Kharkiv
  • Krakow
  • Kyiv
  • Larnaca
  • Lodz
  • Lublin
  • Lviv
  • Odesa
  • Remote.Bulgaria
  • Remote.Georgia
  • Remote.Kazakhstan
  • Remote.Poland
  • Riga
  • Sofia
  • Tbilisi
  • Varna
  • Warsaw
  • Wroclaw
  • Yerevan
Small team (1-10 people)

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Project overview

This project focuses on building a scalable AI platform that transforms expert knowledge into structured graph based intelligence and connects it with enterprise grade language models. The solution emphasizes semantic search, agent workflows, and robust evaluation frameworks to ensure reliable outputs.

Position overview

We are looking for a Senior AI Solutions Architect to lead the design and implementation of advanced LLM driven solutions with a strong focus on retrieval augmented generation and knowledge graph integration. You will take ownership of the full orchestration layer, shaping how structured and unstructured data is transformed into high quality, context aware AI responses.

Technology stack

Python, SQL, vector databases, graph databases, Google Cloud Platform, Cloud Spanner, Vertex AI, Gemini, LLM frameworks, embedding models, observability tools, IAM, encryption

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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