MLOps Architect​/Engineer

We are seeking an experienced MLOps Architect Engineer to design, build, and operate enterprise‑grade Machine Learning Operations (MLOps) platforms.

• 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, Dev Ops, 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.

• 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.

• MLOps Platforms:
  Kubeflow, Vertex AI Pipelines, Sage Maker Pipelines, or MLflow.
• Workflow Orchestration:
  Apache Airflow.
• Containerization & Orchestration:
  Kubernetes, GKE, AKS, or EKS.
• Infrastructure as Code:
  Terraform.
• CI/CD & Dev Ops:
  Git Hub Actions, Git, and CI/CD pipelines.
• Monitoring & Observability:
  Prometheus, model monitoring, and drift detection.
• Programming:
  Python and Bash.
• Cloud Platforms:
  Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services (AWS).
• Version Control & Automation:
  Git Hub, Git Lab, or Azure Dev Ops.

• Certified Kubernetes Administrator (CKA)
• Kubeflow Certified Professional
• Google Professional Machine Learning Engineer
• MLflow Certification
• Databricks Certified MLOps Professional

• 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.

• 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.