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, including data preparation, model training, versioning, deployment, monitoring, governance, and automated retraining. The role requires expertise in cloud-native MLOps, Kubernetes, CI/CD automation, Infrastructure as Code (IaC), and enterprise AI platform engineering to enable reliable, secure, and scalable production AI solutions.
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.
- Document platform architecture, operational standards, deployment procedures, and recovery processes.
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
Responsibilities by Experience Level
- 0 3 Years Support deployment and monitoring of ML models. Build and maintain ML pipelines under senior guidance. Assist with CI/CD implementation and platform automation. Learn Kubernetes, cloud platforms, and Infrastructure as Code.
- 3 6 Years Develop production-grade MLOps pipelines. Implement model versioning, monitoring, and deployment automation. Manage Kubernetes-based ML workloads. Build Infrastructure as Code using Terraform. Improve platform reliability and operational efficiency.
- 6 9 Years Lead enterprise MLOps implementations. Design scalable AI platforms across cloud environments. Standardize CI/CD, governance, monitoring, and operational processes. Mentor junior engineers and collaborate across engineering teams.
- 9 12+ Years Own enterprise MLOps strategy and platform architecture. Define standards for AI platform engineering and lifecycle automation. Lead large-scale AI platform modernization initiatives. Drive governance, security, scalability, and operational excellence. Provide technical leadership across enterprise AI and cloud engineering teams.
Preferred Certifications
- One or more of the following certifications is highly preferred: 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
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. Assist with CI/CD implementation and platform automation. Learn Kubernetes, cloud platforms, and Infrastructure as Code.
- 3 6 Years Develop production-grade MLOps pipelines. Implement model versioning, monitoring, and deployment automation. Manage Kubernetes-based ML workloads. Build Infrastructure as Code using Terraform. Improve platform reliability and operational efficiency.
- 6 9 Years Lead enterprise MLOps implementations. Design scalable AI platforms across cloud environments. Standardize CI/CD, governance, monitoring, and operational processes. Mentor junior engineers and collaborate across engineering teams.
- 9 12+ Years Own enterprise MLOps strategy and platform architecture. Define standards for AI platform engineering and lifecycle automation. Lead large-scale AI platform modernization initiatives. Drive governance, security, scalability, and operational excellence. Provide technical leadership across enterprise AI and cloud engineering teams.
Preferred Certifications
- One or more of the following certifications is highly preferred: 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
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.
