Mozn is a rapidly growing technology firm revolutionizing the field of Artificial Intelligence and Data Science. Headquartered in Riyadh, Saudi Arabia, the company aims to realize Vision 2030 and has a proven track record of supporting and growing the tech ecosystem in Saudi Arabia and the GCC region. Mozn collaborates with large government organizations, corporations, and startups to provide AI-powered products and solutions locally and globally.
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The Principal AI Engineer is a senior technical leader within the Cloud Engineering organization. This role is responsible for shaping and standardizing the company’s approach to agentic AI systems and MLOps excellence. The Principal AI Engineer ensures that all AI workloads (LLMs, agents, and ML models) adhere to unified, production-grade, and compliant standards for scalability, performance, and observability.
What You'll Do:
- Establish and maintain AI and agentic architecture blueprints within Cloud Engineering.
- Standardize AI deployment practices using containerized and serverless inference patterns.
- Lead the adoption of model lifecycle management across environments (Dev → Stage → Prod).
- Collaborate with FinOps and Cloud Security to optimize cost, compliance, and control across AI workloads.
- Own the MLOps reference stack and define CI/CD for AI models.
- Build reusable SDKs, APIs, and templates for AI pipeline integration with Cloud Engineering systems.
- Drive model observability and monitoring standards.
- Lead the design and enablement of agentic AI systems.
- Create reference implementations and governance frameworks for AI workflows.
- Embed AI security, data protection, and compliance into the MLOps lifecycle.
- Mentor AI and ML engineers on scalable design patterns and operational excellence.
Qualifications:
- 10+ years in software, ML, or AI engineering, with 5+ years leading AI or ML systems in production.
- Expertise in Python, PyTorch/TensorFlow, and MLOps frameworks.
- Proven experience with LLM and agentic architectures.
- Familiarity with cloud-native AI stacks and a strong understanding of distributed systems.
- Experience in defining AI infrastructure standards in large or multi-tenant SaaS environments.
Preferred Skills:
- Hands-on experience with vector databases and RAG pipelines.
- Knowledge of AI safety, fairness, and bias mitigation frameworks.
- Graduate degree (MSc/PhD) in Computer Science, Machine Learning, or a related discipline.
Key Traits:
- Platform-first thinking with a strong AI and engineering background.
- Influences across domains for unified AI delivery.
- Obsession with automation, repeatability, and cost-efficient AI operations.
Benefits:
- Competitive compensation and top-tier health insurance.
- A dynamic workplace with opportunities for professional growth.
- A culture that embraces diversity and empowers individuals to be their best selves.
