About the Project

Launching an internal AI initiative to automate business processes, create AI-copilot systems, internal AI assistants, and AI-driven workflow infrastructure.

The company operates in the beauty-tech segment, focusing on professional equipment for the beauty industry, including an international presence.

We are currently forming a small AI-native team that will be responsible for building the company's internal AI infrastructure.

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Role

AI Engineer / Knowledge Engineer

We are looking for an AI Engineer / Knowledge Engineer who will be responsible for the AI knowledge layer and preparing the internal data infrastructure for the company's AI systems.

This is NOT a research ML role.

We need a practical engineer who can:

• Work with data;
• Build RAG systems;
• Prepare an AI-ready knowledge base;
• Create document pipelines;
• Work with embeddings and vector DBs;
• Establish internal AI infrastructure around company data.

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Responsibilities

• Building AI-ready knowledge infrastructure
• Working with internal company data:
• CRM,
• ERP,
• documents,
• email,
• BI,
• Notion,
• spreadsheets,
• internal knowledge bases
• Creating document pipelines
• Building RAG systems
• Working with embeddings and vector databases
• Preparing AI context for copilots and AI assistants
• Creating AI-ready data pipelines
• Working with AI workflow infrastructure
• Developing Python-based AI tooling
• Utilizing AI-assisted development workflows

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Technologies

Python / OpenAI API / Claude API / RAG / Embeddings / Vector DB / LangChain / LangGraph / AI agents / AI workflows / AI-assisted coding: Claude Code, Cursor, Codex, Copilot

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What's Important

• Proficient Python skills
• Understanding of modern AI/LLM systems
• Experience with:
• RAG,
• embeddings,
• vector search,
• AI workflows,
• data processing
• Ability to work with structured and unstructured data
• Practical engineering approach
• Independence
• Ability to quickly deliver results
• Use of AI-assisted coding — must-have

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Will be a plus

Practical ML experience / Fine-tuning / AI evals / Production AI systems / Knowledge systems / Orchestration / automation experience

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Not suitable for

• Pure research ML engineers
• Theoretical ML without practical experience
• No-code only experience
• Slow corporate development without ownership

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Work Format

Full remote / Full-time / International contract / Payment in USDT / Russian-speaking team / English B1+ / Candidates outside of Russia and Belarus

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Who is it for

• AI Systems Engineer
• Knowledge Engineer
• RAG Engineer
• Applied AI Engineer
• AI/ML Engineer with a strong practical mindset
• AI Infra Engineer