Design, build, and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, and continuous improvement in production environments.
Develop backend services and APIs using Python and Java frameworks to operationalize machine learning models.
Implement automated CI/CD workflows for machine learning and data applications.
Manage the full model lifecycle, including feature engineering integration, model registry management, version control, and performance tracking.
Deploy and operate machine learning workloads on Google Cloud Platform using BigQuery, GCS, Dataflow, and Dataproc.
Deploy applications packaged using Docker and orchestrate deployments with Kubernetes.
Implement Infrastructure as Code using Terraform for reproducible environment provisioning.
Establish model observability practices, including drift detection, performance monitoring, and operational reliability controls.
Collaborate with data scientists, platform engineers, and product teams within Agile delivery environments.
Maintain SDLC best practices, including source control, security validation, static analysis, and automated quality checks using GitHub, Tekton, SonarQube, 42Crunch, and FOSSA.
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