AI Engineering Lead

Our Customer Care Squad transforms customer support from reactive to predictive leveraging state-of-the-art AI, Agentic AI, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to provide accurate, real-time, personalized assistance at a massive scale.

As an AI Engineering Lead - Conversational, you will draw on deep, hands-on experience in building and delivering large-scale, production-grade conversational AI and Retrieval-Augmented Generation (RAG) solutions. This role is for an AI expert who has genuinely "been there and done that", someone ready to architect, build, and operate a real-time AI customer support platform with a relentless focus on accuracy, reliability, and ultra-low latency. You'll lead a lean, high-impact team, driving the execution and innovation while ensuring production excellence at every layer of the stack

Help us shape the future of communication by:

• Owning the design and implementation of the AI-driven customer care systems and autonomous multi-agent orchestration workflows.
• Designing, developing, and scaling state-of-the-art cyclic graph agent networks and multi-agent systems using frameworks like LangGraph, CrewAI, or AutoGen.
• Optimizing LLM & Agent execution utilizing advanced runtime techniques such as quantization, pruning, batching, token streaming, and semantic caching to ensure ultra-low latency.
• Owning the solutions alignment of dependencies and service contracts with other teams.
• Designing, developing, and scaling real-time Retrieval-Augmented Generation (RAG) pipelines integrating state-of-the-art open-source LLMs (Llama 3, Mistral, Falcon, or similar).
• Implementing scalable, high-performance vector search (Qdrant, Weaviate, Milvus) for robust knowledge retrieval and semantic search.
• Having awareness of techniques such as quantization, pruning, distillation, batching, and caching for optimizing LLM inference with the minimum response times.
• Developing and exposing secure, performant APIs via FastAPI/gRPC or others, containerized (Docker), orchestrated (Kubernetes), and fully integrated into automated CI/CD pipelines.
• Embedding comprehensive monitoring and evaluation (e.g. MRR, Recall@k, NDCG, Faithfulness, latency metrics) and implementing automated regression testing for continuous improvement.
• Championing and enforcing best practices for data security, compliance (GDPR, Saudi PDPL is a plus), and responsible AI, including PII redaction and end-to-end encryption.
• Demonstrating mastery of foundational software engineering by writing clean code and architecture, maintainable and testable code, designing robust, modular, and scalable systems; leveraging version control, and implementing comprehensive continuous integration, automated testing, and deployment practices.
• Leading rigorous design and code reviews, mentoring engineers, and fostering an innovative engineering culture grounded in clean architecture, SOLID principles, and proactive best practices to ensure system reliability, security, and agility.

What you’ll bring:

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 5+ years delivering production AI/NLP systems, including 2+ years as a technical lead or senior staff engineer.
• Proven experience owning real-time conversational AI/RAG platforms at massive scale, serving thousands of concurrent users.
• Expert proficiency in Java or Python with strong software engineering fundamentals and system-design capabilities.
• Deep knowledge and hands-on experience with frameworks and technologies: PyTorch, Scikit-learn, Hugging Face, LangChain, LlamaIndex, SpringAI (Optional), vector databases (Pinecone, Weaviate, Milvus), and embedding models.
• Strong knowledge of Agentic AI design and tools, e.g. LangGraph, CrewAI, tool calling, and reasoning/thinking models.
• Strong knowledge about context-engineering, and how to design a RAG/chat system memory (long, short, summarized, ...)
• Strong expertise in low-latency inference optimization and GPU resource management.
• Solid experience building large-scale data ingestion and processing pipelines (Spark, Flink, Kafka, RabbitMQ).
• Robust MLOps and deployment expertise (Docker, Kubernetes, MLflow, Kubeflow, Git-based prompt versioning, automated CI/CD).
• Clear communicator capable of translating complex technical concepts into strategic business value.
• Expertise in red-teaming practices and machine learning security research, including developing and reinforcing robust defenses against adversarial threats.
• Arabic & English language proficiency.