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

Robust 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.

Must Have Skill

Docker,Google Kubernetes Engine (GKE),Python,Vertex AI

Good to Have Skill

Other Requirements

📌 Senior Technical Specialist (Chennai)
🏢 HCLTech
📍 Chennai