AI Engineer - RAG & Document Intelligence

the company 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: remote

Employment 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.