Senior Technical Specialist
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Oct 13, 2026.
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.