AI Automation Engineer

R C W M A S Global

Posted: 5 months ago
Openings: 10
Applicants: 1

Job Description

by R C W M A S Global AI Automation Engineer - Careers at R C W M A S Global

AI Automation Engineer Skills

Python R SQL / NoSQL Java / C++ Scikit-learn Natural Language Processing (NLP) Computer Vision Machine Learning (ML) Algorithms Deep Learning Data Wrangling / Cleaning Data Visualization (e.g., Matplotlib, Seaborn, Tableau) Model Deployment (e.g., Flask, FastAPI, Docker) MLOps / CI/CD Pipelines Cloud Platforms (AWS, Azure, GCP) Robotic Process Automation (RPA) Tools: UiPath, Blue Prism, Automation Anywhere Workflow Automation API Integration Business Process Modeling Process Mining Scripting (PowerShell, Bash) Power BI / Tableau

Job Description

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.

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.

by R C W M A S Global