MLOps Architect / Engineer (0–12+ Years Experience)
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Key skills for this role
About the Role
Datamatics Technologies is seeking an MLOps Architect/Engineer to design and operate enterprise-grade MLOps platforms in Riyadh, KSA. The role involves building automated ML pipelines, managing Kubernetes-based workloads, and implementing CI/CD and IaC.
Key Skills for This Role
Responsibilities
- Design and implement enterprise MLOps architecture supporting the complete ML lifecycle
- Build automated ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring
- Develop scalable CI/CD pipelines for ML models and AI applications
- Manage model versioning, experiment tracking, model registry, and artifact management
- Deploy ML workloads on Kubernetes based environments with high availability and scalability
- Implement automated model monitoring, drift detection, performance tracking, and alerting
- Design automated retraining pipelines based on model performance and data drift
- Standardize ML platform governance, security, reproducibility, and operational best practices
- Collaborate with Data Scientists, Data Engineers, AI Engineers, DevOps, and Cloud teams
- Develop Infrastructure as Code (IaC) for cloud based AI platforms
Requirements
- 0–12+ years of experience in MLOps or related field
- Expertise in cloud native MLOps, Kubernetes, CI/CD automation, and Infrastructure as Code
- Proficiency in Python and Bash
- Experience with MLOps platforms (Kubeflow, Vertex AI, SageMaker, MLflow)
- Experience with workflow orchestration (Apache Airflow)
- Experience with monitoring tools (Prometheus, model monitoring, drift detection)
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field (preferred)
- Certifications such as CKA, Kubeflow Certified Professional, or Google Professional ML Engineer (preferred)
Full Job Posting
Job Summary
- We are seeking an experienced MLOps Architect/Engineer to design, build, and operate enterprise grade MLOps platforms.
- The role requires expertise in cloud native MLOps, Kubernetes, CI/CD automation, Infrastructure as Code, and enterprise AI platform engineering.
Key Responsibilities
- Design and implement enterprise MLOps architecture supporting the complete machine learning lifecycle.
- Build automated ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring.
- Develop scalable CI/CD pipelines for machine learning models and AI applications.
- Manage model versioning, experiment tracking, model registry, and artifact management.
- Deploy ML workloads on Kubernetes based environments with high availability and scalability.
- Implement automated model monitoring, drift detection, performance tracking, and alerting.
- Design automated retraining pipelines based on model performance and data drift.
- Standardize ML platform governance, security, reproducibility, and operational best practices.
- Collaborate with Data Scientists, Data Engineers, AI Engineers, DevOps, and Cloud teams.
- Optimize infrastructure utilization, deployment automation, and platform reliability.
- Develop Infrastructure as Code (IaC) for cloud based AI platforms.
- Establish enterprise monitoring, logging, observability, and incident response for ML workloads.
Required Technical Skills
- MLOps Platforms: Kubeflow, Vertex AI Pipelines, SageMaker Pipelines, or MLflow
- Workflow Orchestration: Apache Airflow
- Containerization & Orchestration: Kubernetes (GKE, AKS, or EKS)
- Infrastructure as Code: Terraform
- CI/CD & DevOps: GitHub Actions, Git, CI/CD Pipelines
- Monitoring & Observability: Prometheus, Model Monitoring, Drift Detection
- Programming: Python, Bash
- Cloud Platforms: GCP, Microsoft Azure, or AWS
- Version Control & Automation: GitHub, GitLab, or Azure DevOps
Responsibilities by Experience Level
- 0–3 Years: Support deployment and monitoring of ML models; build and maintain ML pipelines under senior guidance.
- 3–6 Years: Develop production grade MLOps pipelines; implement model versioning, monitoring, and deployment automation.
- 6–9 Years: Lead enterprise MLOps implementations; design scalable AI platforms across cloud environments.
- 9–12+ Years: Own enterprise MLOps strategy and platform architecture; define standards for AI platform engineering.
Preferred Certifications
- Certified Kubernetes Administrator (CKA)
- Kubeflow Certified Professional
- Google Professional Machine Learning Engineer
- MLflow Certification
- Databricks Certified MLOps Professional
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or related discipline.
- Strong understanding of machine learning lifecycle management and production AI systems.
- Experience designing cloud native AI platforms using Kubernetes and Infrastructure as Code.
- Excellent problem solving, collaboration, and technical leadership skills.
- Ability to work in enterprise scale, cross functional, and agile environments.
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