Principal AI Engineer

Reposted 20 Days Ago
Be an Early Applicant
2 Locations
In-Office or Remote
Expert/Leader
Artificial Intelligence • Software • Generative AI
The Role
The Principal AI Engineer leads technical aspects of AI systems and MLOps, establishing best practices for AI workloads and collaborating with cross-functional teams to enhance AI delivery.
Summary Generated by Built In

About Mozn

MOZN is a leading Enterprise AI company enabling organizations to make informed decisions in two critical domains: Financial Crime Prevention and Enterprise Knowledge Intelligence.
We’re a diverse, collaborative team of innovators united by a shared purpose: to build AI that delivers tangible business value, builds trust, and empowers people and organizations with augmented intelligence. Our culture is built on the relentless pursuit of excellence and meaningful impact.
If you’re passionate about working alongside exceptional talent on world-class AI, and you want the autonomy and runway to do the best work of your career, join us in shaping the future of intelligent enterprises.


About the role

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.

What you'll do

  • 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 FinOps 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


Qualifications

  • 10+ years in software, ML, or AI engineering; 5+ years leading AI or ML systems in production
  • Expert in Python, PyTorch/TensorFlow, and MLOps frameworks (Kubeflow, MLflow, Airflow, etc.)
  • Proven experience with LLM and agentic architectures, including LangChain, vLLM, Ray, or similar
  • Experience with cloud-native AI stacks (Vertex AI, SageMaker, 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
Preferred Skills
  • 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
Key Traits
  • Thinks platform-first, ensuring every AI innovation can scale reliably and securely
  • Balances deep AI knowledge with engineering pragmatism and DevOps fluency
  • Influences across domains — from MLOps to Cloud to Security — to enable unified AI delivery
  • Obsessed with automation, repeatability, and cost-efficient AI operations

Benefits

  • You will be at the forefront of an exciting time for the Middle East, joining a high-growth rocket-ship in an exciting space
  • You will be given a lot of responsibility and trust. We believe that the best results come when the people responsible for a function are given the freedom to do what they think is best
  • The fundamentals will be taken care of: competitive compensation, top-tier health insurance, and an enabling culture so that you can focus on what you do best
  • You will enjoy a fun and dynamic workplace working alongside some of the greatest minds in Al
  • We believe strength lies in difference, embracing all for who they are and empowered to be the best version of themselves

Top Skills

Airflow
Aks
Azure Ai
Eks
Gke
Kubeflow
Kubernetes
Mlflow
Mlops
Oci Data Science
Python
PyTorch
Sagemaker
TensorFlow
Vertex Ai
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The Company
HQ: Al Muhammadiyah
361 Employees
Year Founded: 2017

What We Do

Mozn is a Saudi technology company committed to advancing digital humanity through the harnessing of artificial intelligence to build enterprise AI-powered products – FOCAL, the end-to-end Risk and Compliance platform and OSOS, the leading Arabic Gen AI platform – along with tailored AI solutions designed to meet the unique needs of enterprises across various sectors.

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