Job Summary
|
Skill |
Why It Matters |
|
Vertex AI |
Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform. |
|
MLOps & ML Lifecycle Management |
Understanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. |
|
Python & ML Frameworks |
Strong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development. |
|
Data Engineering on GCP |
Knowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines. |
|
Vertex AI Pipelines / Kubeflow |
Building reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. |
|
Containerization & Kubernetes |
Docker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. |
|
Model Monitoring & Observability |
Detecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. |
|
Feature Engineering & Feature Stores |
Reusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability. |
|
CI/CD and Infrastructure as Code |
Terraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases. |
|
Cloud Architecture & Security |
Understanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP. |
Key Responsibilities
|
Skill |
Why It Matters |
|
Vertex AI |
Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform. |
|
MLOps & ML Lifecycle Management |
Understanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. |
|
Python & ML Frameworks |
Strong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development. |
|
Data Engineering on GCP |
Knowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines. |
|
Vertex AI Pipelines / Kubeflow |
Building reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. |
|
Containerization & Kubernetes |
Docker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. |
|
Model Monitoring & Observability |
Detecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. |
|
Feature Engineering & Feature Stores |
Reusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability. |
|
CI/CD and Infrastructure as Code |
Terraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases. |
|
Cloud Architecture & Security |
Understanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP. |
Skill Requirements
|
Skill |
Why It Matters |
|
Vertex AI |
Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform. |
|
MLOps & ML Lifecycle Management |
Understanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. |
|
Python & ML Frameworks |
Strong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development. |
|
Data Engineering on GCP |
Knowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines. |
|
Vertex AI Pipelines / Kubeflow |
Building reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. |
|
Containerization & Kubernetes |
Docker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. |
|
Model Monitoring & Observability |
Detecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. |
|
Feature Engineering & Feature Stores |
Reusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability. |
|
CI/CD and Infrastructure as Code |
Terraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases. |
|
Cloud Architecture & Security |
Understanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP. |