AI Automation Engineer

rcwmas

Kolkata 3 Years Exp Posted 1h ago

Vertex AI pipeline development
Build, manage, and scale Vertex AI Pipelines (Kubeflow / Vertex Workbench) to enable reproducible, robust ML/AI workflows.
Data ingestion & orchestration
Engineer data ingestion flows from various sources into GCS, BigQuery, or Cloud Storage, using Dataflow, Pub/Sub, Composer (Airflow), and Cloud Functions.
Secure data handling
Implement data classification, encryption (at‑rest and in‑transit), IAM governance, and audit logging using Cloud KMS, VPC Service Controls, Cloud DLP.
CI/CD for ML
Automate model builds, testing, deployment using Vertex AI Model Registry, Container Registry, Cloud Build, GitOps tools, and open-source CI/CD.
Infrastructure as Code (IaC)
Use Terraform, Deployment Manager, or CDK to define data and AI infrastructure, incorporating least-privilege policies and reproducibility.
Monitoring & observability
Deploy logging and monitoring using Cloud Monitoring, Logging, APM, Vertex AI Model Monitoring, and alerting for data drift, resource issues, and SLIs/SLOs.
Security reviews & compliance
Conduct threat modeling, risk assessments, align with SOC 2, ISO 27001, HIPAA or GDPR requirements as relevant.
- Team leadership & collaboration
  Mentor junior engineers, define best practices, collaborate cross-functionally with Data Engineering, MLOps, Security, and Product teams.