MLOps Engineer
Core
Building, deploying, monitoring, and maintaining scalable machine learning infrastructure and production-ready AI systems.
Role type
MLOps Engineer
Builds
Scalable MLOps pipelines, production ML models, CI/CD workflows
Domain
AI/ML infrastructure, Cloud Engineering
Deliverable
production ML models
Required skills
Python, Bash, TensorFlow, PyTorch, Scikit-learn, CI/CD, Docker, Kubernetes, Terraform, CloudFormation, Model Monitoring, Model Versioning
Preferred skills
MLflow, Kubeflow, Airflow, Vertex AI Pipelines, Feature Stores, Apache Spark, Prometheus, Grafana, Responsible AI
Technologies
Python, Bash, SQL, TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow, Airflow, DVC, Weights & Biases, Docker, Kubernetes, AWS SageMaker, Microsoft Azure ML, Google Vertex AI, Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, Terraform, CloudFormation, Prometheus, Grafana, ELK Stack, Git, GitHub, GitLab
Responsibilities
Design and maintain scalable MLOps pipelines for model training, deployment, and monitoring; Automate the end-to-end ML lifecycle including data validation, testing, and versioning; Deploy models to production using containerization and orchestration; Monitor model performance and implement retraining strategies; Build and maintain CI/CD pipelines for ML workflows; Manage model versioning, experiment tracking, and artifact repositories; Optimize infrastructure for scalability, reliability, and cost efficiency; Implement security, governance, and compliance best practices; Develop monitoring, logging, and alerting solutions; Troubleshoot production issues related to ML infrastructure.
Seniority
Mid-Senior, hands-on IC
