Senior AI Engineer

About the job

Senior AI Engineer — Generative AI & Azure AI Platform | Hiring via a Hiring Quest

📍 Cairo | 💰 EGP 120K–150K gross/month | 🕓 Immediate Start | Full-Time

Company: Kentrick.ai

About Kentrick.ai

Kentrick.ai is a fast-growing startup building production-grade AI solutions and platforms.

We’re looking for a Senior AI Engineer who can take AI systems beyond prototypes and build reliable, secure, observable, and cost-efficient production solutions.

This is a hands-on senior role requiring strong technical decision-making, end-to-end ownership, and the ability to clearly explain architectural trade-offs.

This Role Is Hiring Through a Hiring Quest

We don’t rely on CVs alone for this position.

Candidates apply through a structured, skills-based Hiring Quest designed to assess how you actually build, reason about, secure, evaluate, and scale AI systems.

The focus is on production GenAI experience, architecture, security, retrieval quality, evaluation, and engineering judgment — not just experience experimenting with LLM APIs.

Job Requirements

We’re looking for a Senior AI Engineer with 8–10 years of overall AI / ML / Data Engineering experience, including strong hands-on production GenAI experience.

Must Have:

• Strong production experience building GenAI systems
• Experience with Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and Azure AI Search
• Strong understanding of production RAG architectures and evidence-grounded generation
• Experience with vector, keyword, hybrid, and semantic retrieval
• Experience with LLM evaluation, observability, and regression analysis
• Strong prompt engineering and orchestration experience
• Experience with open-source foundation models and modern model-serving approaches
• MLOps / LLMOps, containers, APIs, and CI/CD
• Understanding of Responsible AI, indirect prompt injection, and LLM security
• Experience with Entra ID, managed identities, Key Vault, private networking, and secure Azure architectures
• Expert-level Python and strong software engineering practices
• Strong technical communication, prioritization, and end-to-end ownership

You should be comfortable balancing:

Model Quality × Retrieval Quality × Latency × Cost × Security × Reliability × Maintainability

What Is the Hiring Quest?

• Build and evaluate a local RAG prototype.
• Handle real-world enterprise knowledge and security scenarios.
• Design a secure, scalable Azure architecture.
• Submit a short walkthrough explaining your key decisions.
• Top candidates join a 60–90 minute technical review.

Your Quest

Build a production-ready Enterprise Knowledge AI Assistant using the provided document corpus, with a local RAG system that delivers evidence-backed answers while safely handling outdated, conflicting, restricted, and malicious content, then design how the solution would scale securely on Azure.