Ml Engineer

Role & responsibilities

ML ENGINEER - Production MLOps & Model Deployment

Preferred candidate profile

ML ENGINEER - Production MLOps & Model Deployment

Position Title:

Position Summary:

Core Responsibilities:

• Build end-to-end MLOps pipelines using Vertex AI Pipelines and Feature Store
• Implement model versioning, A/B testing, and canary deployments via Vertex AI endpoints
• Develop automated retraining workflows triggered by data drift detection
• Optimize BigQuery queries and implement materialized views for ML feature serving
• Containerize models using Docker and deploy via Vertex AI or Kubernetes Engine
• Integrate model serving APIs with Client’s institutional applications (CRM, LMS)
• Configure model monitoring dashboards tracking accuracy, bias, and business KPIs
• Maintain CI/CD pipelines with Git integration and Azure DevOps sprint alignment
• Document deployment processes and provide knowledge transfer to Client internal teams

Required Technical Skills:

• Vertex AI: Custom containers, pipelines, endpoint management, model monitoring
• BigQuery: Performance optimization, scheduled ML jobs, data versioning
• Python: FastAPI/Flask for model serving, MLflow for experiment tracking
• MLOps Tools: Kubeflow, TFX, or Vertex-native equivalents
• CI/CD: GitHub Actions, Cloud Build, Terraform for IaC
• Containerization: Docker, Kubernetes, Google Kubernetes Engine

Production Excellence:

• Experience deploying models serving >1K predictions/second
• Model governance and compliance frameworks (bias detection, fairness)
• Disaster recovery and model rollback procedures
• Cost optimization for GCP ML workloads

Qualifications:

• Bachelor's/Master's in Computer Science, ML Engineering, or related
• 5+ years MLOps/production ML experience
• Google Cloud Professional ML Engineer certification required
• Demonstrated success deploying education or customer analytics at scale