Senior Ai Engineer - Riyadh,KSA

We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.

This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.

Key Responsibilities

• Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.

• Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.

• Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.

• Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.

• Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.

• Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.

• Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.

• Ensure reproducibility, reliability, and version control across AI/ML environments.

• Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.

• Build repeatable environments using Infrastructure as Code.

• Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.

• Design scalable inference and serving patterns for variable workloads.

• Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.

• Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.

• Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.

• Deliver analytics-ready datasets to downstream systems and applications.

• Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.

• Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.

• Contribute to architecture decisions, technical documentation, best practices, and engineering standards.

• Support knowledge sharing, technical leadership, and continuous improvement across the AI function.