Hire an AI Development Team in Egypt
Dedicated engineers in Cairo who build RAG systems, LLM features, AI agents and the infrastructure to run them, working full-time under your direction for up to 90% less than hiring in Europe or the Gulf.
AI skills we hire for
RAG pipelines
Search and answer over your own documents and data: ingestion, chunking, embeddings, vector databases and evaluation.
LLM integration
Adding language models to your product through APIs such as OpenAI, Anthropic or open-source models, with prompts, guardrails and cost control.
AI agents
Assistants that take multi-step actions: calling your APIs, filling forms, handling support tickets or running internal workflows.
MLOps
Deploying, monitoring and versioning models, with CI/CD, logging and cost tracking in the cloud.
Data engineering
Pipelines that clean and prepare the data your AI features depend on.
Classic machine learning
Forecasting, classification and recommendations with Python, scikit-learn and PyTorch.
Tools and platforms
Python · LangChain / LlamaIndex · OpenAI, Anthropic and open-source models · pgvector, Pinecone or Weaviate · FastAPI · Docker · AWS / Azure / GCP [CONFIRM WHICH TOOLS YOUR TEAM USES]
Sample team shapes
AI feature team
1 AI/ML engineer, 1 back-end developer, part-time QA. Adds an AI feature such as search or a chat assistant to an existing product.
AI product squad
1 tech lead, 2 AI/ML engineers, 1 back-end, 1 front-end, 1 QA. Builds a new AI product from prototype to launch.
Full AI build team
Tech lead, AI/ML engineers, data engineer, back-end, front-end, mobile, QA and MLOps. For founders building an AI-first company.
Which team is right for you?
Example talent profiles
Examples only. Real candidates are shared privately.
Senior AI engineer
[X] years · Python, LLM APIs, RAG, LangChain, pgvector · Built [DETAILS] · English: fluent
MLOps engineer
[X] years · Docker, Kubernetes, AWS, MLflow · [DETAILS] · English: professional
Data engineer
[X] years · Python, SQL, Airflow, data pipelines · [DETAILS] · English: professional
How we vet AI engineers
Portfolio and GitHub review for real AI work [TEAM TO CONFIRM]
Screening call in English [TEAM TO CONFIRM]
Practical task, for example a small RAG or agent build [TEST FORMAT]
Technical interview on architecture, evaluation and cost [TEAM TO CONFIRM]
Your interview and approval
Your data and IP
Your models, prompts, code and data belong to you. Your team works in your own cloud accounts and repositories, under an NDA, and you control every access key.
Frequently asked questions
Can you staff a whole AI team, including a tech lead?
Yes. We can hire the full team, or add AI engineers to a team you already have.
Do your engineers have production AI experience?
We screen for AI work shipped to real users and ask candidates to walk us through it. [CONFIRM HOW YOU VERIFY THIS]
Which models and providers do they work with?
Whichever your product uses: commercial APIs, open-source models, or both. You choose; your team builds to it.
Who pays for model and cloud costs?
You do, on your own accounts. That keeps billing, data and access under your control.
How fast can an AI team start?
Many roles can start within a few days. Senior AI engineers usually need to finish a one-to-two-month notice period first.
Can we start small?
Yes. Many teams start with one or two AI engineers and grow once the first feature ships.
Request a Team
We’ll get back to you as soon as possible with any questions and a time for a short call.