MLOps Architect / Engineer
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Key skills for this role
About the Role
We are seeking an experienced MLOps Architect / Engineer to design, build, and operate enterprise-grade Machine Learning Operations (MLOps) platforms. The ideal candidate will d.
Key Skills for This Role
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
- Develop Infrastructure as Code (IaC) for cloud based AI platforms
Requirements
- Bachelor's or Master's degree in Computer Science, Software Engineering, AI, 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
Full Job Posting
Overview
- We are seeking an experienced MLOps Architect / Engineer to design, build, and operate enterprise grade Machine Learning Operations (MLOps) platforms. The ideal candidate will define and operationalize scalable ML platforms while automating the complete machine learning lifecycle.
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 to accelerate AI solution delivery.
- 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 or Vertex AI Pipelines or SageMaker Pipelines or MLflow
- Workflow Orchestration: Apache Airflow
- Containerization & Orchestration: Kubernetes (GKE or AKS or EKS)
- Infrastructure as Code: Terraform
- CI/CD & DevOps: GitHub Actions and Git and CI/CD Pipelines
- Monitoring & Observability: Prometheus and Model Monitoring and Drift Detection
- Programming: Python and Bash
- Cloud Platforms: Google Cloud Platform (GCP) or Microsoft Azure or Amazon Web Services (AWS)
- Version Control & Automation: GitHub or GitLab or Azure DevOps
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a 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.
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
Expected Deliverables
- Enterprise MLOps Architecture Document
- End to End CI/CD Machine Learning Pipeline
- Production Model Registry
- Model Drift Monitoring & Alerting Framework
- Automated Retraining Pipeline
- Infrastructure as Code (Terraform) Repository
- Kubernetes Deployment Templates
- ML Platform Operational Runbook
- Model Lifecycle Governance Framework
- Monitoring & Observability Dashboard
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