AI Engineer - RAG & Document Intelligence
⚲ Warszawa
170 - 210 PLN/h netto (B2B)
Wymagania
- Python
- RAG
- Azure
- multi-agent AI frameworks
- Databricks
Opis stanowiska
Craftware is a technology company of over 500 experts, empowering large organizations to solve complex business challenges with modern IT solutions - from sales systems and automation to data platforms and AI. We operate where technology must be reliable, secure, and scalable. We deliver end-to-end projects: from analysis and architecture through implementation to development and maintenance. We are a trusted partner of industry leaders such as Salesforce, Veeva, UiPath, and Databricks.
Model: remoteEmployment type: full-time
Role summary
You'll be at the heart of one of the most impactful AI initiatives at an international Consumer Health company. Your mission: build a platform from scratch that lets business users retrieve, synthesize, and act on knowledge locked inside complex enterprise documents — using plain language. This is a greenfield role blending engineering and research, where you design and implement a context-aware, multi-agent AI system that will fundamentally change how the entire organisation interacts with its knowledge assets.
Responsibilities
• Design and build multi-agent AI systems — architect and implement agentic components in Python (routers, planners, verifiers, supervisors) with a focus on composability and adaptability as LLM capabilities evolve.
• Build robust document parsing pipelines — handle PDFs, Word documents, presentations, scanned files, and mixed-format corpora; extract structured meaning from noisy inputs including tables, charts, and figures.
• Architect end-to-end RAG pipelines — own the full stack from document ingestion and semantic chunking, through embedding, indexing, hybrid search, and re-ranking, to dynamic context assembly within token limits.
• Instrument, evaluate, and continuously improve AI quality — build automated regression testing frameworks, support A/B testing of LLM configurations, and integrate human feedback loops.
• Contribute to the AI engineering platform — build reusable frameworks and components, manage production-grade code in GitHub, conduct peer reviews, and contribute to architectural and technology stack decisions.
Requirements
Must-have:
• Strong Python engineering with production-grade Generative AI system experience
• Hands-on experience with multi-agent AI frameworks: LangGraph, LangChain, or Pydantic AI
• Deep experience building RAG pipelines: chunking strategies, embedding models, hybrid search, re-ranking (LightRAG, LlamaIndex, LangChain)
• Solid experience with document parsing and multimodal document understanding (tables, charts, figures)
• Strong API development skills (FastAPI)
• Proficiency with Azure cloud services (Azure Apps, Containers, Storage, AI Search, AI Foundry) and/or AWS equivalents
• Experience with Databricks: Delta Lake, Unity Catalog, MLflow
• Familiarity with AI observability and evaluation frameworks: RAGAS, DeepEval, Langfuse
• Experience with vector databases (pgvector, Pinecone, Qdrant, Weaviate) and Docker containerisation
• Fluent English, both written and spoken
Nice-to-have:
• Hands-on experience with Databricks GenAI products: Vector Search, Agent Framework, Knowledge Assistance, Genie, Agent Bricks
• Experience with LLM context management and prompt engineering
• Knowledge of Model Context Protocol (MCP) for tool integration
• Understanding of CI/CD principles, GitHub Actions, and Infrastructure as Code (Terraform, ARM Templates)
• Understanding of knowledge management, taxonomy design, and metadata enrichment for enterprise document repositories
Why this role is different
• Greenfield from day one — you're building a foundational AI capability from scratch, not maintaining legacy systems or following pre-defined specs.
• Real research component — you'll be answering open architectural questions that genuinely matter; this is applied research, not ticket execution.
• Organisation-wide reach — the platforms you build will serve commercial, marketing, product supply, and R&D teams across a global organisation.
• Cutting-edge stack — multi-agent orchestration, compound AI systems, and LLM-powered document intelligence at enterprise scale.
Employment conditions:
• B2B contract,
• Daily support from team leaders,
• Dedicated certification budget,
• Assistance in defining and support in your development path,
• Benefits package,
• Integration trips/events.
Model: remoteEmployment type: full-time
Role summary
You'll be at the heart of one of the most impactful AI initiatives at an international Consumer Health company. Your mission: build a platform from scratch that lets business users retrieve, synthesize, and act on knowledge locked inside complex enterprise documents — using plain language. This is a greenfield role blending engineering and research, where you design and implement a context-aware, multi-agent AI system that will fundamentally change how the entire organisation interacts with its knowledge assets.
Responsibilities
• Design and build multi-agent AI systems — architect and implement agentic components in Python (routers, planners, verifiers, supervisors) with a focus on composability and adaptability as LLM capabilities evolve.
• Build robust document parsing pipelines — handle PDFs, Word documents, presentations, scanned files, and mixed-format corpora; extract structured meaning from noisy inputs including tables, charts, and figures.
• Architect end-to-end RAG pipelines — own the full stack from document ingestion and semantic chunking, through embedding, indexing, hybrid search, and re-ranking, to dynamic context assembly within token limits.
• Instrument, evaluate, and continuously improve AI quality — build automated regression testing frameworks, support A/B testing of LLM configurations, and integrate human feedback loops.
• Contribute to the AI engineering platform — build reusable frameworks and components, manage production-grade code in GitHub, conduct peer reviews, and contribute to architectural and technology stack decisions.
Requirements
Must-have:
• Strong Python engineering with production-grade Generative AI system experience
• Hands-on experience with multi-agent AI frameworks: LangGraph, LangChain, or Pydantic AI
• Deep experience building RAG pipelines: chunking strategies, embedding models, hybrid search, re-ranking (LightRAG, LlamaIndex, LangChain)
• Solid experience with document parsing and multimodal document understanding (tables, charts, figures)
• Strong API development skills (FastAPI)
• Proficiency with Azure cloud services (Azure Apps, Containers, Storage, AI Search, AI Foundry) and/or AWS equivalents
• Experience with Databricks: Delta Lake, Unity Catalog, MLflow
• Familiarity with AI observability and evaluation frameworks: RAGAS, DeepEval, Langfuse
• Experience with vector databases (pgvector, Pinecone, Qdrant, Weaviate) and Docker containerisation
• Fluent English, both written and spoken
Nice-to-have:
• Hands-on experience with Databricks GenAI products: Vector Search, Agent Framework, Knowledge Assistance, Genie, Agent Bricks
• Experience with LLM context management and prompt engineering
• Knowledge of Model Context Protocol (MCP) for tool integration
• Understanding of CI/CD principles, GitHub Actions, and Infrastructure as Code (Terraform, ARM Templates)
• Understanding of knowledge management, taxonomy design, and metadata enrichment for enterprise document repositories
Why this role is different
• Greenfield from day one — you're building a foundational AI capability from scratch, not maintaining legacy systems or following pre-defined specs.
• Real research component — you'll be answering open architectural questions that genuinely matter; this is applied research, not ticket execution.
• Organisation-wide reach — the platforms you build will serve commercial, marketing, product supply, and R&D teams across a global organisation.
• Cutting-edge stack — multi-agent orchestration, compound AI systems, and LLM-powered document intelligence at enterprise scale.
Employment conditions:
• B2B contract,
• Daily support from team leaders,
• Dedicated certification budget,
• Assistance in defining and support in your development path,
• Benefits package,
• Integration trips/events.
🔍 Dekoder Ogłoszenia
🔴
build a platform from scratch
Będziesz odpowiedzialny za stworzenie całego systemu od podstaw, co może oznaczać brak istniejącej infrastruktury i dokumentacji.
🔴
blending engineering and research
Oczekuje się od Ciebie zarówno implementacji technicznej, jak i eksploracji nowych rozwiązań, co może wymagać czasu na badania i eksperymenty.
🔴
design and implement a context-aware, multi-agent AI system
Projektowanie i wdrażanie złożonego systemu z wieloma współpracującymi agentami AI, co wymaga głębokiego zrozumienia architektury i interakcji między komponentami.
🔴
fundamentally change how the entire organisation interacts with its knowledge assets
Twoja praca będzie miała znaczący wpływ na sposób pracy całej firmy, co może wiązać się z presją i koniecznością zarządzania zmianą.
🔴
composability and adaptability as LLM capabilities evolve
System musi być elastyczny i łatwy do modyfikacji w miarę rozwoju technologii LLM, co wymaga projektowania z myślą o przyszłych zmianach.