{"id":4022728,"date":"2026-09-24T13:26:15","date_gmt":"2026-09-24T11:26:15","guid":{"rendered":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/ml-ai-engineer-llm-rag-agentic-tools\/"},"modified":"2026-09-25T23:28:30","modified_gmt":"2026-09-25T21:28:30","slug":"ml-ai-engineer-llm-rag-agentic-tools-cyberharbor-tech-09-24-2026","status":"publish","type":"job","link":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/ml-ai-engineer-llm-rag-agentic-tools-cyberharbor-tech-09-24-2026\/","title":{"rendered":"ML\/AI Engineer (LLM, RAG, Agentic Tools)"},"content":{"rendered":"

We are looking for a specialist who will be responsible for the development and maintenance of a complex AI-powered search system built on top of a large interactive knowledge base. The system includes tool calling and agentic behavior that interact with internal tools and multiple types of storage: vector databases for semantic search, document-oriented databases for full-text search, as well as relational and graph databases for lookup by specific identifiers. The search interface is built as an LLM-powered chat that supports natural language queries and returns well-grounded conclusions, recommendations, and links to primary sources.<\/p>\n

The candidate will be responsible for designing and implementing the full LLM-powered search pipeline, including orchestration, reasoning, reranking, tool calling, handling tool outputs, validating results, and generating final user-facing responses.<\/p>\n

We expect the candidate to:
Have hands-on commercial experience working with vLLM in self-hosted environments, including model optimization on H100\/H200 GPUs, with a solid understanding of GPU architecture principles and cache behavior under production workloads.
Have a deep understanding of the RAG concept and a proven track record of successfully deploying similar solutions in production.
Have worked with Haystack, LlamaIndex, LangChain, or similar frameworks, and understand the core differences between them.
Have experience using MCP and working with LLMs that call external tools and services.
Have strong Python skills, including architectural principles, design patterns, multithreading, asynchronous programming, modern testing practices, and experience operating systems under load (monitoring, memory optimization, telemetry).
Have experience working with document-oriented, relational, and vector databases, including an understanding of index types and the appropriate use cases for each.<\/p>\n

It would be a plus if you have:
Experience building or maintaining knowledge bases, AI-powered reporting systems, or chatbots with large context windows.
Experience training or fine-tuning LLMs or compact models for domain-specific tasks.
Practical DevOps experience: CI\/CD, Kubernetes, Helm, ArgoCD.
Experience developing OCR- and Vision-Language-based systems for processing unstructured data.
Experience in data engineering: understanding of ETL\/ELT processes, working with orchestrators (Airflow, Temporal, Prefect), and distributed computing systems (Ray, Spark).
Experience working with Elasticsearch, MongoDB, PostgreSQL, Qdrant\/Milvus, Neo4j, and other database systems.
Understanding of cybersecurity processes and modern approaches to data protection.<\/p>\n

We offer a fully remote work format on our own product, which uses state-of-the-art technologies and is focused on the public sector. The company has the status of being critically important to the Armed Forces of Ukraine. If needed, we provide military deferment and the necessary office equipment.<\/p>\n

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\u0423 \u0437\u043e\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u0456 \u043a\u0430\u043d\u0434\u0438\u0434\u0430\u0442\u0430 \u0432\u0445\u043e\u0434\u0438\u0442\u0438\u043c\u0435 \u043f\u0440\u043e\u0454\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0442\u0430 \u0432\u043f\u0440\u043e\u0432\u0430\u0434\u0436\u0435\u043d\u043d\u044f \u043f\u043e\u0432\u043d\u043e\u0433\u043e \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0443 LLM-powered \u043f\u043e\u0448\u0443\u043a\u0443, \u0432\u043a\u043b\u044e\u0447\u043d\u043e \u0437 orchestration, reasoning, reranking, tool calling, \u043e\u0431\u0440\u043e\u0431\u043a\u043e\u044e \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0435\u0439 \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0456\u0432, \u0432\u0430\u043b\u0456\u0434\u0430\u0446\u0456\u0454\u044e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0456\u0432 \u0442\u0430 \u0444\u043e\u0440\u043c\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0444\u0456\u043d\u0430\u043b\u044c\u043d\u0438\u0445 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0435\u0439 \u0434\u043b\u044f \u043a\u043e\u0440\u0438\u0441\u0442\u0443\u0432\u0430\u0447\u0430.<\/p>\n

\u041c\u0438 \u043e\u0447\u0456\u043a\u0443\u0454\u043c\u043e, \u0449\u043e \u043a\u0430\u043d\u0434\u0438\u0434\u0430\u0442:
\u041c\u0430\u0454 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u0439 \u043a\u043e\u043c\u0435\u0440\u0446\u0456\u0439\u043d\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 vLLM \u0443 self-hosted \u0441\u0435\u0440\u0435\u0434\u043e\u0432\u0438\u0449\u0430\u0445, \u0432\u043a\u043b\u044e\u0447\u043d\u043e \u0437 \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u0454\u044e \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043d\u0430 GPU H100\/200, \u0440\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f\u043c \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0456\u0432 GPU-\u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440 \u0442\u0430 \u043f\u043e\u0432\u0435\u0434\u0456\u043d\u043a\u0438 \u043a\u0435\u0448\u0443 \u043f\u0456\u0434 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d-\u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u043d\u044f\u043c.
\u0413\u043b\u0438\u0431\u043e\u043a\u043e \u0440\u043e\u0437\u0443\u043c\u0456\u0454 \u043a\u043e\u043d\u0446\u0435\u043f\u0446\u0456\u044e RAG \u0442\u0430 \u043c\u0430\u0454 \u0434\u043e\u0441\u0432\u0456\u0434 \u0443\u0441\u043f\u0456\u0448\u043d\u043e\u0433\u043e \u0432\u043f\u0440\u043e\u0432\u0430\u0434\u0436\u0435\u043d\u043d\u044f \u043f\u043e\u0434\u0456\u0431\u043d\u0438\u0445 \u0440\u0456\u0448\u0435\u043d\u044c \u0443 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u0435\u043d\u0456.
\u041f\u0440\u0430\u0446\u044e\u0432\u0430\u0432 \u0437 Haystack \/ LlamaIndex \/ LangChain \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u043c\u0438 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430\u043c\u0438 \u0442\u0430 \u043e\u0440\u0456\u0454\u043d\u0442\u0443\u0454\u0442\u044c\u0441\u044f \u0432 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u043e\u0432\u0438\u0445 \u0432\u0456\u0434\u043c\u0456\u043d\u043d\u043e\u0441\u0442\u044f\u0445 \u043c\u0456\u0436 \u043d\u0438\u043c\u0438.
\u041c\u0430\u0454 \u0434\u043e\u0441\u0432\u0456\u0434 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f MCP \u0442\u0430 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 LLM, \u044f\u043a\u0456 \u0432\u0438\u043a\u043b\u0438\u043a\u0430\u044e\u0442\u044c \u0437\u043e\u0432\u043d\u0456\u0448\u043d\u0456 \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0438 \u0439 \u0441\u0435\u0440\u0432\u0456\u0441\u0438.
\u0412\u043e\u043b\u043e\u0434\u0456\u0454 Python \u043d\u0430 \u0432\u0438\u0441\u043e\u043a\u043e\u043c\u0443 \u0440\u0456\u0432\u043d\u0456: \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u043d\u0456 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0438, \u0448\u0430\u0431\u043b\u043e\u043d\u0438 \u043f\u0440\u043e\u0454\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f, \u0431\u0430\u0433\u0430\u0442\u043e\u043f\u043e\u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044c, \u0430\u0441\u0438\u043d\u0445\u0440\u043e\u043d\u043d\u0456\u0441\u0442\u044c, \u0441\u0443\u0447\u0430\u0441\u043d\u0456 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0438 \u0442\u0435\u0441\u0442\u0443\u0432\u0430\u043d\u043d\u044f, \u0430 \u0442\u0430\u043a\u043e\u0436 \u0434\u043e\u0441\u0432\u0456\u0434 \u0435\u043a\u0441\u043f\u043b\u0443\u0430\u0442\u0430\u0446\u0456\u0457 \u0441\u0438\u0441\u0442\u0435\u043c \u043f\u0456\u0434 \u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u043d\u044f\u043c (\u043c\u043e\u043d\u0456\u0442\u043e\u0440\u0438\u043d\u0433, \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f \u043f\u0430\u043c'\u044f\u0442\u0456, \u0442\u0435\u043b\u0435\u043c\u0435\u0442\u0440\u0456\u044f).
\u041c\u0430\u0454 \u0434\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u043e\u043e\u0440\u0456\u0454\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u043c\u0438, \u0440\u0435\u043b\u044f\u0446\u0456\u0439\u043d\u0438\u043c\u0438 \u0442\u0430 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u0438\u043c\u0438 \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u0438\u0445, \u0432\u043a\u043b\u044e\u0447\u043d\u043e \u0437 \u0440\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f\u043c \u0442\u0438\u043f\u0456\u0432 \u0456\u043d\u0434\u0435\u043a\u0441\u0456\u0432 \u0456 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0438\u0445 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u0457\u0432 \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f.<\/p>\n

\u0411\u0443\u0434\u0435 \u043f\u043b\u044e\u0441\u043e\u043c:
\u0414\u043e\u0441\u0432\u0456\u0434 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0430\u0431\u043e \u043f\u0456\u0434\u0442\u0440\u0438\u043c\u043a\u0438 \u0431\u0430\u0437 \u0437\u043d\u0430\u043d\u044c, \u0441\u0438\u0441\u0442\u0435\u043c \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0456\u0457 \u0437\u0432\u0456\u0442\u0456\u0432 \u043d\u0430 \u0431\u0430\u0437\u0456 AI, \u0440\u043e\u0437\u0440\u043e\u0431\u043a\u0430 \u0447\u0430\u0442\u0431\u043e\u0442\u0456\u0432 \u0456\u0437 \u0432\u0435\u043b\u0438\u043a\u0438\u043c \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u043d\u0438\u043c \u0432\u0456\u043a\u043d\u043e\u043c.
\u0414\u043e\u0441\u0432\u0456\u0434 \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0430\u0431\u043e fine-tuning LLM \u0447\u0438 \u043a\u043e\u043c\u043f\u0430\u043a\u0442\u043d\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0441\u043f\u0435\u0446\u0438\u0444\u0456\u0447\u043d\u0438\u0445 \u0437\u0430\u0434\u0430\u0447.
\u041f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0443 DevOps: CI\/CD, Kubernetes, Helm, ArgoCD.
\u0414\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0437\u0440\u043e\u0431\u043a\u0438 \u0441\u0438\u0441\u0442\u0435\u043c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 OCR \u0442\u0430 Vision-Language \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u043e\u0431\u0440\u043e\u0431\u043a\u0438 \u043d\u0435\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0432\u0430\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445.
\u0414\u043e\u0441\u0432\u0456\u0434 \u0443 data engineering: \u0440\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f ETL\/ELT-\u043f\u0440\u043e\u0446\u0435\u0441\u0456\u0432, \u0440\u043e\u0431\u043e\u0442\u0430 \u0437 \u043e\u0440\u043a\u0435\u0441\u0442\u0440\u0430\u0442\u043e\u0440\u0430\u043c\u0438 (Airflow, Temporal, Prefect) \u0442\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u043c\u0438 \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b\u0435\u043d\u043e\u0433\u043e \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f (Ray.io, Spark).
\u0414\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 Elasticsearch, MongoDB, PostgreSQL, Qdrant\/Milvus, Neo4j \u0442\u0430 \u0456\u043d\u0448\u0438\u043c\u0438 \u0421\u0423\u0411\u0414.
\u0420\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f \u043f\u0440\u043e\u0446\u0435\u0441\u0456\u0432 \u043a\u0456\u0431\u0435\u0440\u0431\u0435\u0437\u043f\u0435\u043a\u0438 \u0442\u0430 \u0441\u0443\u0447\u0430\u0441\u043d\u0438\u0445 \u043f\u0456\u0434\u0445\u043e\u0434\u0456\u0432 \u0434\u043e \u0437\u0430\u0445\u0438\u0441\u0442\u0443 \u0434\u0430\u043d\u0438\u0445.<\/p>\n

\u041f\u0440\u043e\u043f\u043e\u043d\u0443\u0454\u043c\u043e \u043f\u043e\u0432\u043d\u0456\u0441\u0442\u044e \u0432\u0456\u0434\u0434\u0430\u043b\u0435\u043d\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 \u0440\u043e\u0431\u043e\u0442\u0438 \u043d\u0430\u0434 \u0432\u043b\u0430\u0441\u043d\u0438\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u043c, \u044f\u043a\u0438\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u043d\u0430\u0439\u0441\u0443\u0447\u0430\u0441\u043d\u0456\u0448\u0456 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457 \u0442\u0430 \u043e\u0440\u0456\u0454\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0439 \u043d\u0430 \u0434\u0435\u0440\u0436\u0430\u0432\u043d\u0438\u0439 \u0441\u0435\u043a\u0442\u043e\u0440. \u041a\u043e\u043c\u043f\u0430\u043d\u0456\u044f \u043c\u0430\u0454 \u0441\u0442\u0430\u0442\u0443\u0441 \u043a\u0440\u0438\u0442\u0438\u0447\u043d\u043e \u0432\u0430\u0436\u043b\u0438\u0432\u043e\u0457 \u0434\u043b\u044f \u0417\u0431\u0440\u043e\u0439\u043d\u0438\u0445 \u0441\u0438\u043b \u0423\u043a\u0440\u0430\u0457\u043d\u0438. \u0417\u0430 \u043f\u043e\u0442\u0440\u0435\u0431\u0438 \u043d\u0430\u0434\u0430\u0454\u043c\u043e \u0431\u0440\u043e\u043d\u044e\u0432\u0430\u043d\u043d\u044f \u0442\u0430 \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0443 \u043e\u0444\u0456\u0441\u043d\u0443 \u0442\u0435\u0445\u043d\u0456\u043a\u0443.<\/p>\n","protected":false},"excerpt":{"rendered":"

We are looking for a specialist who will be responsible…<\/p>\n","protected":false},"author":13907,"featured_media":0,"parent":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_mo_disable_npp":"","rwu_salary_min":0,"rwu_salary_max":0,"rwu_salary_currency":"","rwu_salary_period":"","rwu_english_level":"B2","rwu_experience_months":36,"rwu_candidate_countries":"\u0423\u043a\u0440\u0430\u0457\u043d\u0430","rwu_company_type":"\u041f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0456\u044f","rwu_industry":"miltech","rwu_part_time":false,"rwu_source_views":233,"rwu_source_applications":41,"rwu_source_category":"ML AI","rwu_source_name":"Djinni.co","rwu_source_url":"https:\/\/djinni.co\/jobs\/787909-ml-ai-engineer-llm-rag-agentic-tools\/"},"job_category":[57],"job_type":[54],"class_list":["post-4022728","job","type-job","status-publish","hentry","job_category-programuvannia","job_type-povna-zainiatist"],"yoast_head":"\nML\/AI Engineer (LLM, RAG, Agentic Tools), (\u0432\u0456\u0434\u0434\u0430\u043b\u0435\u043d\u043e) | Remote Work Ukraine<\/title>\n<meta name=\"description\" content=\"\u0412\u0456\u0434\u0434\u0430\u043b\u0435\u043d\u0430 \u0432\u0430\u043a\u0430\u043d\u0441\u0456\u044f ML\/AI Engineer (LLM, RAG, Agentic Tools) \u0443 . 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