AI Engineering Lead - Conversational

Unifonic

Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.

Our Customer Care Squad transforms customer support from reactive to predictive leveraging state-of-the-art 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.

• 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, REMAIL_ADDRESS, 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.