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.

Other Requirements

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.

Senior Technical Specialist

What we know about this role

Skills required

Job description

Job Summary

Key Responsibilities

Skill Requirements

Other Requirements

Tools to land this role

Ground Reality

Spot the dealbreakers in this 668-word post before you apply

Insider Path

Find someone who can refer you here

Get tomorrow's jobs before you have to search

Someone shared one job with you. ASAI keeps finding the rest.

Senior Technical Specialist

What we know about this role

Skills required

Job description

Job Summary

Key Responsibilities

Skill Requirements

Other Requirements

Tools to land this role

Ground Reality

Spot the dealbreakers in this 668-word post before you apply

Insider Path

Find someone who can refer you here

Get tomorrow's jobs before you have to search

Someone shared one job with you. ASAI keeps finding the rest.