Role Description We are seeking talented Data & AI Engineers to support the development of an enterprise-grade AI Assistant. This is a high-impact initiative at the intersection of modern data engineering and applied AI. The role focuses on building the data pipeline that transforms unstructured corporate documents into a searchable, intelligent knowledge base powering a Large Language Model (LLM) as well as automating actions such as ticket creation and workflow integrations. - Data Engineering: Build and maintain scalable data pipelines using Databricks and Azure Data Lake Storage Gen2 (ADLS Gen2), ensuring reliability and performance across all pipeline stages. - Data Transformation: Implement the Medallion Architecture (Raw → Silver → Gold layers) to clean, normalise, and structure technical documentation for downstream AI consumption. - AI Orchestration: Design and develop the Reasoning Layer using frameworks such as LangChain or LlamaIndex to manage LLM logic, prompt routing, and tool orchestration. - Search Optimisation: Manage and tune Vector Databases (e.g. Pinecone, Weaviate, or Azure AI Search) to enable fast and accurate semantic retrieval across the corporate knowledge base. - Integration & Automation: Develop Azure Apps and Azure Functions to connect the AI system to external enterprise platforms such as Salesforce and SharePoint, enabling end-to-end automation. Qualifications - 3–4 years in Data Engineering or Software Development with a strong focus on cloud-based data processing. - Strong proficiency in Databricks and Python; comfortable working across the full data engineering lifecycle. - Hands-on experience with LLM orchestration frameworks: LangChain, LlamaIndex, or similar. - Solid understanding of the Azure ecosystem — Storage (ADLS Gen2), Identity (AAD), and Serverless (Azure Functions). - Familiarity with modern AI coding assistants (GitHub Copilot, Cursor, OpenAI Codex) to accelerate development velocity. Company Description Cypher Consulting Europe takes pride in its ability to deliver expert consultants across a wide spectrum of technologies and industry sectors.

• Lead the design and evolution of our enterprise data architecture • Define and lead enterprise data architecture strategy across Azure data platforms, Snowflake, Azure Data Lake, analytics, and integration services • Design scalable, secure, and high-performance data models, pipelines, and platform architectures aligned to business and technical requirements • Establish architecture standards, governance frameworks, and best practices for data quality, lineage, security, and lifecycle management • Oversee solution design reviews, architecture documentation, and implementation guidance for critical data initiatives • Provide technical leadership and mentorship to data engineers, architects, and business intelligence professionals

• The Data Engineer designs, builds, and maintains scalable data pipelines and analytics solutions across our Azure cloud environment • This role transforms raw data from multiple source systems into reliable, business-ready information that powers reporting, analytics, and operational decision-making • The Data Engineer works closely with analysts, application developers, and business stakeholders to deliver high-quality data solutions on Snowflake, Azure Data Lake, and Microsoft Power BI • Design, develop, and maintain ETL/ELT pipelines in Azure to ingest, transform, and integrate data from multiple source systems • Build and optimize Snowflake-based data solutions for scalable storage, transformation, and analytics workloads • Manage and support Azure Data Lake structures for both structured and unstructured data • Develop interactive dashboards and reports in Microsoft Power BI to support business intelligence and operational reporting • Implement integration solutions that connect Azure services, Snowflake, and downstream reporting platforms • Monitor pipeline performance, troubleshoot failures, and implement enhancements to improve efficiency and reliability • Ensure data quality, integrity, security, and consistency across all cloud data platforms • Partner with analysts, developers, and business stakeholders to gather and refine data and reporting requirements

• Partner with business to gather requirements and implement solutions • Collaborate with IT teams and prioritize projects • Lead cross-functional teams and manage multiple projects • Establish system documentation standards and quality assurance methodologies