Hands on experience doing ML Ops job duties, deploying ML apps
Experience with AWS services: Lambda, Sagemaker, CodeCommit, etc.
Experience with Databricks and model serving
Proficient in Python
Minimum Requirements
hands-on experience in MLOps deploying ML applications in production at scale.
Proficient in AWS services: SageMaker, Lambda, CodePipeline, CodeCommit, ECR, ECS/Fargate, and CloudWatch.
Strong experience with Databricks workflows and Databricks Model Serving, including MLflow for model tracking, packaging, and deployment.
Proficient in Python and shell scripting with the ability to containerize applications using Docker.
Deep understanding of CI/CD principles for ML, including testing ML pipelines, data validation, and model quality gates.
Hands-on experience orchestrating ML workflows using Airflow (open-source or MWAA) or Databricks Workflows.
Familiarity with model monitoring and logging stacks (e.g., Prometheus, ELK, Datadog, or OpenTelemetry).
Experience deploying models as REST endpoints, batch jobs, and asynchronous workflows.
Version control expertise with Git/GitHub and experience in automated deployment reviews and rollback strategies.
Nice to Have
Experience with Feature Store (e.g., AWS SageMaker Feature Store, Feast).
Familiarity with Kubeflow, SageMaker Pipelines, or Vertex AI (if multi-cloud).
Exposure to LLM-based models, vector databases, or retrieval-augmented generation (RAG) pipelines.
Knowledge of Terraform or AWS CDK for infrastructure automation.
Experience with A/B testing or shadow deployments for ML models.