AI Engineer

We are looking for a skilled and practical AI Engineer to design, build, and maintain local AI agents, private datasets, and intelligent automation solutions for our internal operations and customer-facing projects.

The ideal candidate should be able to work with Large Language Models
, local AI deployment
, retrieval-augmented generation
, vector databases
, document processing
, and workflow automation
. This role is especially important for building secure AI systems that can run locally or privately, using company data without exposing sensitive information to public AI platforms.

The engineer will work closely with management, sales, technical teams, and operations to convert business knowledge, documents, processes, and customer data into usable AI agents and internal productivity tools.

Design and build AI agents for internal and customer use cases, such as:

• Technical presales assistant
• Proposal and BoQ generation assistant
• ISO 9001 / compliance assistant
• Customer support knowledge agent
• Project documentation assistant
• Network and cybersecurity advisory assistant
• CRM and operations automation agents

Build AI workflows that can connect with internal systems such as:

• Email
• Document repositories
• Helpdesk systems
• Project management tools
• Knowledge bases
• Internal databases

Develop agents that can perform tasks such as document search, summarization, classification, recommendation, data extraction, report generation, and workflow triggering.

Implement AI models and applications that can run in private or local environments, including:

• Private cloud
• Local workstations

Evaluate, deploy, and optimize open-source LLMs such as:

• Mistral
• Qwen
• Gemma
• Deep Seek
• Other suitable open-source models

Work with local AI tools and frameworks such as:

• LM Studio
• vLLM
• Lang Chain
• Auto Gen / CrewAI or similar agent frameworks

Ensure AI systems are secure, scalable, reliable, and aligned with company data privacy requirements.

Create, clean, structure, and maintain private datasets from company documents, including:

• Technical proposals
• Bo Qs
• SOPs
• ISO documents
• CRM records
• Network and cybersecurity solution documents

Build and maintain searchable knowledge bases using:

• Embeddings
• Retrieval-augmented generation
• Document chunking
• Data classification
• Access controls

Work with vector databases such as:

• ChromaDB
• Qdrant
• Pinecone
• Weaviate
• FAISS
• PostgreSQL with pgvector

Develop user-friendly AI tools, dashboards, and internal applications using technologies such as:

• Python
• FastAPI
• React / Next.js
• Node.js
• REST APIs
• Docker

Build integrations with third-party systems through APIs, webhooks, and automation platforms.

Create proof-of-concepts and convert successful prototypes into production-ready tools.

Ensure company and customer data is handled securely.

Implement controls for:

• Data privacy
• User access
• Audit trails
• Prompt logging
• Hallucination reduction
• Secure API usage
• Backup and version control

Help define internal standards for using AI safely across the company.

The candidate should have:

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI, or related field.
• 2+ years of experience in AI, machine learning, data engineering, or software development.
• Practical experience with LLMs and AI application development.
• Experience with APIs, databases, and automation workflows.
• Experience building RAG systems or document-based AI search.
• Good understanding of embeddings, vector databases, and prompt engineering.
• Ability to work with unstructured documents such as PDFs, Word files, Excel sheets, emails, and knowledge base articles.
• Ability to turn business requirements into working AI solutions.
• Good documentation and communication skills.

Strong candidates will also have experience with:

• Local LLM deployment
• Cybersecurity or IT infrastructure knowledge
• Microsoft 365 / Google Workspace integrations
• Docker and Linux environments
• Cloud platforms such as AWS, Azure, or Google Cloud
• Fine-tuning or model optimization
• OCR and document intelligence
• Arabic and English language AI processing
• Building AI agents for sales, presales, support, or operations
• Working in system integrator, MSP, cybersecurity, or IT services environments