Job Brief
Konvergense is an equal opportunity employer and welcomes applicants from all backgrounds, nationalities, and professional experiences.
Konvergense is a UAE-based AI automation and digital agency founded in 2008. We help businesses across the UAE and GCC implement AI automation solutions that use business data, documents, systems, and knowledge to improve customer experience, productivity, and operational performance.
We are looking for a skilled Data Engineer / AI Knowledge Systems Specialist to join our team. This role is ideal for someone who can prepare, structure, manage, and connect business data for AI agents, knowledge-base systems, RAG applications, internal copilots, chatbots, and enterprise automation solutions.
The ideal candidate should understand data pipelines, databases, document processing, knowledge-base architecture, embeddings, vector search, data quality, access control, and AI-ready information systems.
Position Overview
The Data Engineer / AI Knowledge Systems Specialist will be responsible for building and maintaining the data and knowledge layer behind Konvergense’s AI automation solutions.
You will work with client documents, websites, FAQs, product information, policies, manuals, CRM data, spreadsheets, databases, and internal knowledge sources to make them usable by AI systems.
This role is especially important for enterprise, healthcare, education, finance, real estate, retail, and government-related projects where AI systems must provide accurate, controlled, and context-aware responses based on approved business information.
Key Responsibilities
Data Preparation & Structuring
- Collect, clean, organize, and structure client data from documents, websites, spreadsheets, CRMs, databases, cloud drives, and internal systems.
- Prepare data for AI agents, chatbots, virtual assistants, internal copilots, and RAG-based knowledge systems.
- Identify data gaps, inconsistencies, duplication, outdated content, and poor-quality information.
- Create structured datasets, metadata, taxonomies, and knowledge categories.
- Support data transformation, enrichment, validation, and normalization.
AI Knowledge Base Development
- Build and maintain AI-ready knowledge bases using client-approved documents, FAQs, manuals, product information, policies, service descriptions, and operational data.
- Support document ingestion, chunking, embedding generation, indexing, retrieval, and search optimization.
- Work with vector databases and semantic search tools to improve AI answer accuracy.
- Design knowledge structures that help AI systems retrieve the right information at the right time.
- Support multilingual and bilingual knowledge systems where required, including English and Arabic content.
RAG & Retrieval Optimization
- Work with AI / LLM Engineers to build Retrieval-Augmented Generation systems.
- Test retrieval quality, response grounding, source accuracy, and answer relevance.
- Improve chunking strategies, metadata tagging, document ranking, and retrieval logic.
- Help reduce hallucination risk by improving data structure and knowledge retrieval.
- Support evaluation of AI responses using test queries, benchmarks, and client-approved answer sets.
Data Integration & Governance
- Connect AI systems to business data sources such as CRMs, ERPs, helpdesk platforms, spreadsheets, databases, cloud storage, websites, and document repositories.
- Support data access control, user permissions, source tracking, auditability, and privacy requirements.
- Help define what information AI systems can access, retrieve, or display.
- Support secure handling of sensitive business data.
- Work with technical teams to ensure data flows are reliable, controlled, and aligned with client requirements.
Documentation & Collaboration
- Work closely with AI consultants, AI engineers, automation engineers, project managers, and client stakeholders.
- Prepare data maps, knowledge-base structures, data source documentation, ingestion notes, and maintenance guidelines.
- Support client workshops related to data readiness and AI knowledge requirements.
- Help create internal processes for AI knowledge-base setup, data validation, and ongoing updates.
Educational Qualifications
- Bachelor’s degree in Data Science, Computer Science, Information Systems, Software Engineering, Artificial Intelligence, Statistics, Business Analytics, or a related field.
- Equivalent professional experience may be considered for candidates with strong data engineering or AI knowledge-system experience.
- Certifications in data engineering, cloud platforms, AI, analytics, or database technologies are an advantage.
Professional Experience
- Minimum 3–5 years of professional experience in data engineering, data management, business intelligence, knowledge management, database administration, AI data preparation, or related fields.
- Experience preparing data for AI systems, chatbots, search systems, RAG applications, or analytics platforms is strongly preferred.
- Experience working with documents, unstructured data, enterprise knowledge bases, CRM data, or internal business repositories is preferred.
- Experience with client-facing, agency, consulting, SaaS, or enterprise technology environments is an advantage.
- UAE/GCC project experience is preferred, especially in industries such as real estate, healthcare, education, retail, hospitality, finance, or professional services.
Technical Knowledge & Skills
Required Technical Skills
- Strong understanding of data cleaning, structuring, transformation, and validation.
- Experience with SQL and relational databases.
- Familiarity with Python for data processing, scripting, or automation.
- Understanding of unstructured data, documents, PDFs, spreadsheets, web content, and knowledge-base preparation.
- Experience with embeddings, vector search, semantic search, or RAG concepts.
- Ability to work with APIs, databases, cloud storage, and structured/unstructured data sources.
- Understanding of data privacy, access control, permissions, and secure information handling.
Preferred Technical Exposure
- Vector databases such as Pinecone, Weaviate, Qdrant, Chroma, FAISS, Supabase, or PostgreSQL with vector extensions.
- Data tools and platforms such as BigQuery, Snowflake, PostgreSQL, MySQL, MongoDB, Firebase, Supabase, or Airtable.
- Cloud platforms such as AWS, Azure, or Google Cloud.
- Document processing tools, OCR tools, knowledge-base platforms, and search systems.
- LLM platforms such as OpenAI, Anthropic, Gemini, Azure OpenAI, or similar tools.
- LangChain, LlamaIndex, or similar frameworks.
- CRM and business platforms such as HubSpot, Zoho, Salesforce, Microsoft Dynamics, SharePoint, Google Drive, Notion, Confluence, Zendesk, or Freshdesk.
Skills and Other Requirements
- Strong analytical thinking and attention to detail.
- Ability to organize complex information clearly.
- Strong problem-solving skills and ability to identify data quality issues.
- Understanding of how business knowledge must be structured for AI use.
- Ability to work with both technical and non-technical stakeholders.
- Strong documentation and process-mapping ability.
- Good communication skills.
- Ability to manage multiple data sources and client projects.
- Fluency in English is required. Arabic language capability is an advantage.
- UAE/GCC market experience is preferred.
Additional Preferences
The following will be considered a strong advantage:
- Experience building RAG systems, AI knowledge bases, internal search systems, or document-based AI assistants.
- Experience working with bilingual English-Arabic data.
- Experience with enterprise data governance, regulated data environments, or sensitive business information.
- Experience in healthcare, education, finance, real estate, retail, hospitality, or government-related projects.
- Ability to design knowledge-base maintenance processes for long-term AI system accuracy.
- Experience creating data documentation, taxonomies, metadata structures, and retrieval evaluation methods.
What We Are Looking For
We are looking for someone who understands that AI is only as useful as the data and knowledge behind it. The right candidate should be able to take messy, scattered, inconsistent business information and turn it into structured, AI-ready knowledge.
You should be comfortable working with documents, databases, business teams, developers, and AI systems. You should be able to think carefully about accuracy, access control, information quality, and retrieval performance.
This role requires a detail-oriented data professional who can help Konvergense build AI automation solutions that are accurate, reliable, and grounded in real business knowledge.
Location
UAE / GCC region preferred.
Candidates based in the UAE will be given preference. Remote or hybrid working arrangements may be considered for highly qualified candidates with strong data engineering or AI knowledge-system experience.
How to Apply
Qualified and interested candidates are invited to apply by submitting their resume, cover letter, and relevant portfolio, data project examples, AI knowledge-base examples, RAG system experience, or technical documentation through the adjoining application submission module.
Please include the position title “Data Engineer / AI Knowledge Systems Specialist” in your application.
Applications should highlight relevant experience in data engineering, knowledge-base development, RAG systems, document processing, data structuring, AI data preparation, and enterprise data projects.
Shortlisted candidates may be asked to complete a short data structuring or AI knowledge-base design assessment.