AI Engineer
Job Summary
The AI Engineer is responsible for designing, building, evaluating, and optimizing AI-driven systems with a strong focus on large language models (LLMs). This role requires hands-on experience in AI evaluations, prompt optimization, Retrieval-Augmented Generation (RAG) pipelines, and autonomous agent architectures, ensuring scalable, reliable, and high-performance AI solutions in production environments.
Key Responsibilities
AI System Design & Development
- Design, implement, and maintain AI-powered applications using large language models.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines for accurate, grounded responses.
- Develop autonomous agents and multi-agent systems to solve complex workflows and tasks.
Prompt Engineering & Optimization
- Design, test, and systematically optimize prompts to improve model performance and reliability.
- Establish prompt iteration frameworks using quantitative and qualitative evaluation methods.
- Implement strategies to handle large context windows while minimizing context degradation and memory loss.
Evaluation & Performance Monitoring
- Set up and maintain AI evaluation (Evals) frameworks to measure model quality, accuracy, and consistency.
- Define evaluation metrics and automated testing pipelines for continuous improvement.
- Analyze model outputs and iterate based on performance insights.
Infrastructure & Reliability
- Handle rate limits, streaming responses, retries, and error handling in production systems.
- Implement logging, monitoring, and fallback mechanisms for robust AI services.
- Optimize latency, cost, and throughput of AI model usage.
Collaboration & Best Practices
- Work closely with software engineers, data teams, and product stakeholders to integrate AI solutions.
- Stay current with emerging AI tools, frameworks, and best practices.
- Contribute to internal documentation, standards, and reusable AI components.
Required Qualifications (Updated)
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Proven experience building and deploying LLM-based systems in real-world applications.
- Hands-on experience with prompt engineering, AI evaluation (Evals), and systematic prompt optimization workflows.
- Experience with Azure AI Foundry or similar cloud-based AI platforms (e.g., AWS, GCP).
- Experience in fine-tuning, deploying, and maintaining custom Large Language Models (LLMs).
- Hands-on experience working with Arabic-language trained language models and Arabic text processing.
Technical Skills
- Strong experience designing and implementing RAG pipelines (vector databases, embeddings, retrieval strategies).
- Experience building autonomous agents and multi-agent systems.
- Deep understanding of prompt optimization techniques and evaluation methodologies.
- Proficiency in handling large context windows, memory management, and context-compression strategies.
- Experience managing API rate limits, streaming responses, retries, and error handling.
- Proficiency in Python and modern AI/ML tooling.
Preferred Skills
- Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, or similar).
- Familiarity with orchestration frameworks for agents and workflows.
- Experience deploying AI systems in cloud or production environments.
- Understanding of AI safety, reliability, and responsible AI practices.
Key Competencies
- Strong problem-solving and analytical skills
- Systematic experimentation and evaluation mindset
- Ability to translate business needs into AI solutions
- Attention to reliability, scalability, and performance
