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Lead a team building a knowledge graph that maps scientific experts from publications and clinical trials using graph databases, vector embeddings, and LLM workflows to power expert discovery and AI-driven insights.
Build and maintain AI-ready knowledge systems by cleaning, structuring, and connecting client data for RAG chatbots and enterprise copilots using vector search and embeddings.
Builds AI-powered features end-to-end: designs LLM agents, integrates models (OpenAI/Anthropic), and crafts responsive UIs with Vue 3/React and TypeScript.
Зарплата: 190000–210000 RUR (на руки) Компания ЕМЕ – разработчик и интегратор решений для управления логистикой. Мы в ТОП-3 вендоров и интеграторов WMS в России. ● 34 года в области разработки систем управления…
Leads architecture and engineering for an AI data engine that accelerates ML dataset generation and warehouse operations for autonomous-vehicle models, using cloud infrastructure and agentic AI workflows.
Job Description Education and Work Experience Requirements: · 5 to 8 years of experience as Data Scientist or GenAI specialist· 2 to 3 years of experience in Generative AI solution development· Proven track record and…
Education and Work Experience Requirements: · 5 to 8 years of experience as Data Scientist· 2 to 3 years of experience in Generative AI solution development· Strong understanding of AI agent collaboration, negotiation,…
Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global…
Build and ship AI agents end-to-end: design LLM-powered backends with RAG, vector search, and event streaming, then expose them via modern Vue/React frontends.
Design and lead enterprise-scale AI and data architectures, integrating LLMs, RAG, and agentic systems with cloud platforms like AWS, Azure, and Google Cloud.
Build AI-powered multi-agent systems and full-stack applications using Node.js, React, and TypeScript, integrating LLMs, vector databases, and observability tools like Langfuse for agentic workflows and dashboards.
Designs enterprise AI solutions on Microsoft’s stack, setting standards and guiding teams to build secure, scalable GenAI apps using Azure AI, Copilot Studio, and modern frameworks.
Lead enterprise GenAI strategy, architect scalable AI systems, and advise C-suite on tech stacks, trade-offs, and ROI for large client engagements.
Обязанности: Разрабатывать системы анализа здоровья: наше ключевое направление AI, который помогает пользователю понять своё состояние и вовремя дойти до врача; Внедрять AI-фичи в продукт и в компанию: интеграция LLM…
Build full-stack web apps that embed generative AI features—modern UIs, conversational agents, and LLM APIs—while shipping secure, scalable backends on Azure or Google Cloud.
Build AI-powered full-stack apps with .NET Core and React, using AI tools to analyze requirements, generate code, and deliver production features like conversational assistants and RAG systems.
Build and scale the backend and infrastructure that ingests, processes, and detects anomalies in 100% of agent conversation data using TypeScript, ClickHouse, and Quickwit.
Design and deliver enterprise-scale AI/ML and generative AI systems, including RAG, agents, and Snowflake Cortex integrations, while leading architecture reviews and client engagements.
Build and deploy ML/AI models end-to-end, from data exploration to production, using Python, PyTorch/TensorFlow, and cloud platforms like AWS/GCP/Azure.
Build and deploy AI tools to automate workflows across trading, operations, and customer success using LLMs and agentic systems, shipping production-ready solutions quickly.
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