
Lucidya
Staff / Senior AI Engineer (Video AI & LLM Systems)
- Permanent
- Riyadh, Saudi Arabia
- Experience 5 - 10 yrs
Job expiry date: 20/04/2026
Job overview
Date posted
06/03/2026
Location
Riyadh, Saudi Arabia
Salary
Undisclosed
Compensation
Job description
The Staff / Senior AI Engineer (Video AI & LLM Systems) role at Lucidya operates within the artificial intelligence software and B2B SaaS customer experience intelligence industry, focusing on building next-generation multimodal AI capabilities that extend the company’s CXM platform into advanced video intelligence. Lucidya is an AI-native customer experience platform that manages entire customer lifecycles autonomously, from initial engagement through retention and growth, using proprietary Natural Language Understanding (NLU) capabilities trained on millions of multilingual conversations. The platform empowers marketing, support, customer experience, and research teams to deliver personalized experiences that improve customer satisfaction, retention, and lifetime value. This role contributes to a major strategic product direction: expanding the platform’s capabilities beyond traditional social and enterprise data analysis into comprehensive video intelligence. The engineer will design and build systems capable of understanding sentiment, context, and intent across multimodal inputs including visual signals, audio signals, and text data extracted from video content. Responsibilities include architecting end-to-end video analysis pipelines integrating Computer Vision, Video Analysis, and Large Language Models to extract semantic meaning from multimodal content. The position also involves owning the deployment, operation, and optimization of self-hosted LLM infrastructure to comply with Saudi data regulations requiring private model hosting environments. This includes implementing containerized deployment strategies using Docker and Kubernetes across cloud or on-premise infrastructure, managing inference pipelines, and optimizing performance factors such as latency, scalability, reliability, cost efficiency, and uptime. The role requires building production-grade ML systems that operate reliably beyond experimental notebooks, including full lifecycle ownership of modeling, infrastructure, deployment, and monitoring processes. As a senior technical contributor, the engineer influences architecture decisions, engineering standards, and system reliability while mentoring mid-level and junior engineers and providing technical guidance through code reviews and architecture discussions. The position operates in a fast-paced engineering environment where engineers collaborate with Product teams to define technical roadmaps, translate conceptual ideas such as video listening capabilities into deployable product features, and continuously deliver measurable improvements to enterprise customer experiences. Additional technical exposure areas may include Arabic multimodal NLP, OCR for Arabic text within video content, generative video models or diffusion models, and Edge AI optimization techniques. Success in the role is measured by deployed production systems and real-world feature adoption rather than experimental research output, requiring strong expertise in machine learning engineering, multimodal systems integration, and large-scale production AI deployment.
Required skills
Key responsibilities
- Design and implement end-to-end video analysis pipelines integrating Computer Vision, Video Analysis, audio processing, and text analysis to extract sentiment, intent, and semantic meaning from multimodal video data
- Develop production-grade multimodal AI systems that combine visual, audio, and textual signals with Large Language Models to generate contextual understanding and actionable insights from video content
- Deploy machine learning models into secure production environments using containerized infrastructure technologies such as Docker and Kubernetes across cloud infrastructure or on-premise infrastructure
- Build, operate, and optimize self-hosted LLM infrastructure required to comply with Saudi data regulations by hosting models privately and maintaining controlled deployment environments
- Manage model inference pipelines while balancing performance trade-offs related to scalability, latency, cost efficiency, reliability, and uptime for enterprise AI workloads
- Collaborate with Product teams to define technical roadmaps for new capabilities such as video intelligence and video listening features and convert conceptual product ideas into production-ready platform features
- Own the full lifecycle of machine learning systems including modeling, infrastructure setup, deployment, monitoring, and long-term maintenance of production ML systems
- Mentor mid-level and junior engineers, review code, contribute to architectural decision-making, and elevate engineering standards across the AI engineering team through technical leadership
Experience & skills
- Possess 5+ years of experience in Machine Learning for Senior level or 7–8+ years for Staff level with deep technical expertise in Computer Vision, Video Analysis, Large Language Models, and Multimodal AI systems
- Demonstrate experience shipping real machine learning systems into production environments rather than only building experimental prototypes or research models
- Deploy and manage machine learning models in containerized environments using technologies such as Docker and Kubernetes
- Operate and maintain AI infrastructure on cloud infrastructure or on-premise infrastructure environments supporting scalable production deployments
- Host and optimize Large Language Models or other large-scale machine learning models in controlled private infrastructure environments to ensure security and regulatory compliance
- Manage inference pipelines and performance optimization processes including scalability tuning, latency reduction, reliability improvements, and infrastructure monitoring
- Work effectively in fast-moving product engineering environments by defining direction under ambiguity, validating assumptions, and delivering production-ready AI features
- Communicate technical concepts clearly and participate in architecture discussions, technical reviews, and engineering alignment processes to guide platform evolution