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
Job Description:
Building, scaling, automating and orchestrating model pipelines; Experience in specific tech stacks include: MLFlow, AutoML, MosaicML, Seldon, Airflow, Docker, Kubernetes, Helm or similar, AWS Sagemaker, Databricks, Grafana or similar, Tecton or similar, CUDA or similar
MLOps Engineer (AWS & Databricks)
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Primary Responsibilities
Design, implement, and maintain CI/CD pipelines for machine learning applications using AWS CodePipeline, CodeCommit, and CodeBuild.
Automate the deployment of ML models into production using Amazon SageMaker, Databricks, and MLflow for model versioning, tracking, and lifecycle management.
Develop, test, and deploy AWS Lambda functions for triggering model workflows, automating pre/post-processing, and integrating with other AWS services.
Maintain and monitor Databricks model serving endpoints, ensuring scalable and low-latency inference workloads.
Use Airflow (MWAA) or Databricks Workflows to orchestrate complex, multi-stage ML pipelines, including data ingestion, model training, evaluation, and deployment. Collaborate with Data Scientists and ML Engineers to productionize models and convert notebooks into reproducible and version-controlled ML pipelines. Integrate and automate model monitoring (drift detection, performance logging) and alerting mechanisms using tools like CloudWatch, Prometheus, or Datadog. Optimize compute workloads by managing infrastructure-as-code (IaC) via CloudFormation or Terraform for reproducible, secure deployments across environments.
Ensure secure and compliant deployment pipelines using IAM roles, VPC, and secrets management with AWS Secrets Manager or SSM Parameter Store.
Champion DevOps best practices across the ML lifecycle, including canary deployments, rollback strategies, and audit logging for model changes.
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