MLOps Engineer
Core
Design, develop, and maintain scalable MLOps pipelines for machine learning model development, deployment, monitoring, and retraining to connect data science with software engineering.
Role type
Senior IC MLOps Engineer
Builds
Production-ready AI solutions, scalable ML infrastructure, and automated deployment pipelines
Domain
Cloud engineering, Machine Learning Operations, Generative AI
Deliverable
production ML models
Required skills
Python, Linux, shell scripting, Git, Docker, Kubernetes, CI/CD pipeline design, cloud platform management (AWS/Azure/GCP), model deployment, REST APIs, microservices architecture, workflow orchestration, feature stores, model registries, observability tools, Terraform, LLMOps, RAG, vector databases
Preferred skills
MLOps platforms (MLflow, Kubeflow, SageMaker, Vertex AI, Databricks), distributed training frameworks (Ray, DeepSpeed), GPU infrastructure optimization, open-source MLOps contributions
Technologies
Airflow, AWS, Azure, CI/CD, Cloud, Databricks, Docker, ELK, Git, GitHub, GitLab, Grafana, Jenkins, Kubeflow, Kubernetes, Linux, Machine Learning, MLflow, MLOps, Model Training, OpenTelemetry, Prometheus, Python, RAG, REST, Terraform, microservices, SQL, Security
Responsibilities
Design and maintain scalable MLOps pipelines; Automate model training, testing, validation, deployment, monitoring, and retraining workflows; Build and manage CI/CD pipelines for ML applications; Deploy and manage ML models in cloud and Kubernetes environments; Implement model versioning, experiment tracking, and artifact management; Monitor model performance, data drift, latency, and system health; Develop feature stores, model registries, and automated retraining pipelines; Optimize infrastructure costs and resource utilization; Maintain technical documentation and operational best practices
Seniority
Mid-to-Senior, hands-on IC
