As the Lead AI Automation Engineer, you will architect and oversee the deployment of AI-driven automation across data ingestion, transformation, model training, serving, and monitoring pipelines. You’ll ensure all processes meet high standards for data security, privacy, and regulatory compliance.

? 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.

Must-Have ✅ Qualifications & Skills

- 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, Skilled 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.

📌 AI Automation Engineer (Kolkata)
🏢 R C W M A S Global
📍 Kolkata