Principal AI Engineer

About Mozn

Mozn is a rapidly growing technology firm revolutionizing the field of Artificial Intelligence and Data Science headquartered in Riyadh, Saudi Arabia and it's working to realise Vision 2030 with a proven track record of excellence in supporting and growing the tech ecosystem in Saudi Arabia and the GCC region. Mozn is the trusted AI technology partner for some of the largest government organizations, as well as many large corporations and startups.

We are in an exciting stage of scaling the company to provide AI-powered products and solutions both locally and globally that ensure the growth and prosperity of our digital humanity. It is an exciting time to work in the field of AI to create a long-lasting impact.

The Principal AI Engineer is a senior technical leader within the Cloud Engineering organization, responsible for shaping and standardizing the company’s approach to agentic AI systems and MLOps excellence. This role bridges AI innovation and platform engineering — ensuring that all AI workloads (LLMs, agents, and ML models) follow unified, production-grade, and compliant standards for scalability, performance, and observability.

You will define the blueprints, frameworks, and reference architectures for AI workloads across all product lines, partnering closely with AI researchers, data scientists, and platform engineers to enable secure, efficient, and repeatable AI delivery.

• Establishing and maintaining AI and agentic architecture blueprints (RAG, orchestration, fine-tuning, prompt pipelines, etc.) within Cloud Engineering
• Standardizing AI deployment practices using containerized and serverless inference patterns
• Leading the adoption of model lifecycle management across environments (Dev → Stage → Prod)
• Partnering with Fin Ops and Cloud Security to optimize cost, compliance, and control across AI workloads
• Owning the MLOps reference stack (e.g., MLflow, Kubeflow, Ray, Vertex AI, or custom platform)
• Defining CI/CD for AI models including versioning, artifact tracking, and retraining workflows
• Building reusable SDKs, APIs, and templates for AI pipeline integration with Cloud Engineering systems
• Driving model observability and monitoring standards for drift, latency, and data integrity
• Leading the design and enablement of agentic AI systems (LLM-driven orchestrators, tool-using agents, multi-agent frameworks)
• Creating reference implementations and governance frameworks for RAG, memory, and action-based AI workflows.
• Collaborating with product and data teams to move prototypes into secure, production-grade environments
• Embedding AI security, data protection, and PDPL/GDPR compliance into the MLOps lifecycle
• Defining model validation and explainability standards, ensuring auditability and traceability
• Working with Cloud Security and Data teams on responsible AI controls and AIOps monitoring
• Mentoring AI and ML engineers on scalable design patterns and operational excellence
• Contributing to internal AI guilds, tech councils, and engineering playbooks.
• Representing Cloud Engineering in AI ecosystem evaluations and cross-functional initiatives

• 10+ years in software, ML, or AI engineering; 5+ years leading AI or ML systems in production
• Expert in Python, PyTorch/Tensor Flow, and MLOps frameworks (Kubeflow, MLflow, Airflow, etc.)
• Proven experience with LLM and agentic architectures, including Lang Chain, vLLM, Ray, or similar
• Experience with cloud-native AI stacks (Vertex AI, Sage Maker, Azure AI, OCI Data Science)
• Strong understanding of distributed systems, data pipelines, and cloud orchestration (Kubernetes, GKE, EKS, AKS)
• Track record of defining AI infrastructure standards in large or multi-tenant SaaS environments

• Hands‑on with vector databases (Pinecone, FAISS, Weaviate) and RAG pipelines
• Familiarity with AI cost optimization, GPU utilization metrics, and inference scaling
• Knowledge of AI safety, fairness, and bias mitigation frameworks
• Graduate degree (MSc/PhD) in Computer Science, Machine Learning, or a related discipline

• Thinks platform‑first, ensuring every AI innovation can scale reliably and securely
• Balances deep AI knowledge with…