{"id":4022732,"date":"2026-09-24T13:26:14","date_gmt":"2026-09-24T11:26:14","guid":{"rendered":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/ml-ai-engineer-ocr-vl-models\/"},"modified":"2026-09-25T21:19:08","modified_gmt":"2026-09-25T19:19:08","slug":"ml-ai-engineer-ocr-vl-models-cyberharbor-tech-09-24-2026","status":"publish","type":"job","link":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/ml-ai-engineer-ocr-vl-models-cyberharbor-tech-09-24-2026\/","title":{"rendered":"ML\/AI Engineer (OCR, VL Models)"},"content":{"rendered":"

Our company is developing its own interactive knowledge base. Users can upload any volume of diverse data, after which the system automatically transforms it into structured, searchable content. To achieve this, we use a comprehensive ML pipeline involving OCR, vision-language models, document parsers, and multiple types of data storage. Search is implemented through an LLM-based chat interface that allows users to submit queries in natural language, retrieve relevant documents, and receive summarized findings and recommendations based on the uploaded materials.<\/p>\n

We are currently looking for an ML\/AI Engineer with deep experience working with unstructured content – primarily photographs and scanned document copies – who will strengthen our team and lead the development of pipelines and solutions for processing this type of data.<\/p>\n

We expect the candidate to:
Have broad experience working with unstructured data and have used both classical OCR systems (Tesseract, PaddleOCR, EasyOCR) and modern models (PaddleOCR-VL, DeepSeek-OCR, dots.ocr, Qwen3-VL). Understand the strengths and weaknesses of different architectures and the scenarios in which each is most effective.
Have hands-on experience training and fine-tuning OCR and Vision-Language models.
Understand algorithmic approaches to image enhancement, preprocessing, and filtering in order to improve downstream recognition quality.
Have worked with Docling (including advanced backend tuning for parsing complex PDFs and tables), markitdown, Apache Tika, PyMuPDF, and other tools for document conversion and structuring. Be comfortable with processing layered and non-trivial PDF files.
Have experience working with self-hosted models, including deploying and optimizing models in vLLM environments on H100-class GPUs and above, and understand the principles of GPU architecture and KV cache.
Have strong Python programming skills, including architectural patterns, multithreading, asynchronous programming, modern testing practices, and experience operating services under high load (monitoring, telemetry, memory utilization).
Have experience working with document-oriented, relational, and vector databases, and understand index types and the relevant use cases for each type of storage.<\/p>\n

It would be a plus if you have:
Experience working with Speech-to-Text or Video-to-Text pipelines.
Experience in content analysis, including classification, summarization, and NER.
Experience in data engineering: understanding of ETL\/ELT processes, experience with orchestrators (Airflow, Temporal, Prefect), and distributed computing systems (Ray, Spark).
Experience in DevOps: CI\/CD, Kubernetes, Helm, ArgoCD.
Experience working with Elasticsearch, MongoDB, PostgreSQL, Qdrant\/Milvus, Neo4j, and other storage systems.
Understanding of modern cybersecurity approaches.
Hands-on experience with production-grade RAG-based systems.<\/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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\u0417\u0430\u0440\u0430\u0437 \u043c\u0438 \u0448\u0443\u043a\u0430\u0454\u043c\u043e ML\/AI \u0456\u043d\u0436\u0435\u043d\u0435\u0440\u0430 \u0437 \u0433\u043b\u0438\u0431\u043e\u043a\u0438\u043c \u0434\u043e\u0441\u0432\u0456\u0434\u043e\u043c \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 \u043d\u0435\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0432\u0430\u043d\u0438\u043c \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u043e\u043c (\u043d\u0430\u0441\u0430\u043c\u043f\u0435\u0440\u0435\u0434 \u0444\u043e\u0442\u043e\u0433\u0440\u0430\u0444\u0456\u044f\u043c\u0438 \u0442\u0430 \u0441\u043a\u0430\u043d-\u043a\u043e\u043f\u0456\u044f\u043c\u0438 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0456\u0432), \u044f\u043a\u0438\u0439 \u043f\u0456\u0434\u0441\u0438\u043b\u0438\u0442\u044c \u043d\u0430\u0448\u0443 \u043a\u043e\u043c\u0430\u043d\u0434\u0443 \u0442\u0430 \u043e\u0447\u043e\u043b\u0438\u0442\u044c \u043d\u0430\u043f\u0440\u044f\u043c \u0440\u043e\u0437\u0440\u043e\u0431\u043a\u0438 \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0456\u0432 \u0456 \u0440\u0456\u0448\u0435\u043d\u044c \u0434\u043b\u044f \u043e\u0431\u0440\u043e\u0431\u043a\u0438 \u0442\u0430\u043a\u0438\u0445 \u0434\u0430\u043d\u0438\u0445.<\/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 \u0448\u0438\u0440\u043e\u043a\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 \u043d\u0435\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0432\u0430\u043d\u0438\u043c\u0438 \u0434\u0430\u043d\u0438\u043c\u0438 \u0442\u0430 \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u0432\u0430\u0432 \u044f\u043a \u043a\u043b\u0430\u0441\u0438\u0447\u043d\u0456 OCR-\u0441\u0438\u0441\u0442\u0435\u043c\u0438 (Tesseract, PaddleOCR, EasyOCR), \u0442\u0430\u043a \u0456 \u0441\u0443\u0447\u0430\u0441\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u0456 (PaddleOCR-VL, DeepSeek-OCR, dots.ocr, Qwen3-VL). \u0420\u043e\u0437\u0443\u043c\u0456\u0454 \u0441\u0438\u043b\u044c\u043d\u0456 \u0442\u0430 \u0441\u043b\u0430\u0431\u043a\u0456 \u0441\u0442\u043e\u0440\u043e\u043d\u0438 \u0440\u0456\u0437\u043d\u0438\u0445 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440 \u0456 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u0457 \u0457\u0445 \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f.
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\u041f\u0440\u0430\u0446\u044e\u0432\u0430\u0432 \u0437 Docling (\u0432\u043a\u043b\u044e\u0447\u043d\u043e \u0437 \u0442\u043e\u043d\u043a\u0438\u043c \u043d\u0430\u043b\u0430\u0448\u0442\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0431\u0435\u043a\u0435\u043d\u0434\u0456\u0432 \u0434\u043b\u044f \u043f\u0430\u0440\u0441\u0438\u043d\u0433\u0443 \u0441\u043a\u043b\u0430\u0434\u043d\u0438\u0445 PDF \u0456 \u0442\u0430\u0431\u043b\u0438\u0446\u044c), markitdown, Apache Tika, PyMuPDF \u0442\u0430 \u0456\u043d\u0448\u0438\u043c\u0438 \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438 \u0434\u043b\u044f \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0456\u0457 \u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0438\u0437\u0430\u0446\u0456\u0457 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0456\u0432. \u041e\u0440\u0456\u0454\u043d\u0442\u0443\u0454\u0442\u044c\u0441\u044f \u0443 \u043f\u0440\u043e\u0446\u0435\u0441\u0438\u043d\u0433\u0443 \u0431\u0430\u0433\u0430\u0442\u043e\u0448\u0430\u0440\u043e\u0432\u0438\u0445 \u0456 \u043d\u0435\u0442\u0440\u0438\u0432\u0456\u0430\u043b\u044c\u043d\u0438\u0445 PDF-\u0444\u0430\u0439\u043b\u0456\u0432.
\u041c\u0430\u0454 \u0434\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 self-hosted \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438, \u0437\u043e\u043a\u0440\u0435\u043c\u0430 \u0437\u0430\u043f\u0443\u0441\u043a\u0443 \u0442\u0430 \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u0457 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0443 \u0441\u0435\u0440\u0435\u0434\u043e\u0432\u0438\u0449\u0430\u0445 vLLM \u043d\u0430 GPU \u0440\u0456\u0432\u043d\u044f H100 \u0456 \u0432\u0438\u0449\u0435, \u0440\u043e\u0437\u0443\u043c\u0456\u0454 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0438 GPU-\u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0438 \u0442\u0430 KV cache.
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\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, \u0440\u043e\u0437\u0443\u043c\u0456\u0454 \u0442\u0438\u043f\u0438 \u0456\u043d\u0434\u0435\u043a\u0441\u0456\u0432 \u0456 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0456 \u044e\u0437\u043a\u0435\u0439\u0441\u0438 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0442\u0438\u043f\u0443 \u0441\u0445\u043e\u0432\u0438\u0449.<\/p>\n

\u0411\u0443\u0434\u0435 \u043f\u043b\u044e\u0441\u043e\u043c:
\u0414\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 Speech-to-text \u0430\u0431\u043e Video-to-text \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430\u043c\u0438.
\u0414\u043e\u0441\u0432\u0456\u0434 \u0430\u043d\u0430\u043b\u0456\u0437\u0443 \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u0443: \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044f, \u0441\u0443\u043c\u0430\u0440\u0438\u0437\u0430\u0446\u0456\u044f, NER.
\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 \u0443 DevOps: CI\/CD, Kubernetes, Helm, ArgoCD.
\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 \u0441\u0445\u043e\u0432\u0438\u0449\u0430\u043c\u0438.
\u0420\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f \u0441\u0443\u0447\u0430\u0441\u043d\u0438\u0445 \u043f\u0456\u0434\u0445\u043e\u0434\u0456\u0432 \u0434\u043e \u043a\u0456\u0431\u0435\u0440\u0431\u0435\u0437\u043f\u0435\u043a\u0438.
\u041f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0456\u0437 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u043c\u0438 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 RAG \u0443 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u0435\u043d\u0456.<\/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":"

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Users…<\/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":172,"rwu_source_applications":13,"rwu_source_category":"ML AI","rwu_source_name":"Djinni.co","rwu_source_url":"https:\/\/djinni.co\/jobs\/809210-ml-ai-engineer-ocr-vl-models\/"},"job_category":[57],"job_type":[54],"class_list":["post-4022732","job","type-job","status-publish","hentry","job_category-programuvannia","job_type-povna-zainiatist"],"yoast_head":"\nML\/AI Engineer (OCR, VL Models), (\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 (OCR, VL Models) \u0443 . 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