Senior Technical Specialist

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.

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.

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.