AI Automation Engineer

🛠 Core Responsibilities

• 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.

Job Requirement

✅ Qualifications & Skills

Must-Have:

• 0 to 3 years in engineering or MLOps roles, with hands-on experience building production workflows in GCP.
• Deep experience with Vertex AI, Kubeflow Pipelines, or Kubeflow on GKE.
• Proficiency in Python, Terraform (or comparable IaC tools), SQL.
• Strong knowledge of GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Functions, Cloud Storage, Secret Manager, IAM, KMS, VPC, etc.
• Expertise in secure data workflows: encryption, compliance frameworks, identity and access management.
• Experience implementing CI/CD automation for AI/ML systems.

Nice-to-Have:

• Certifications such as Google Cloud Professional Data Engineer, Professional Cloud Architect, or MLOps Engineering Specialist.
• Familiarity with Docker, Kubernetes, Kubernetes-native orchestration.
• Knowledge of GitOps tooling: ArgoCD, Flux, or Jenkins X.
• Experience with data cataloguing tools like Data Catalog, DataGov, Great Expectations, or similar.
• Statistical understanding of model evaluation, drift detection, bias mitigation.