Senior Technical Specialist
HCLTech
Senior Technical Specialist
About this role
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
Frequently Asked Questions
Is the salary disclosed for the Senior Technical Specialist position at HCLTech?
The salary for this Senior Technical Specialist role at HCLTech is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Senior Technical Specialist position at HCLTech located?
This Senior Technical Specialist role at HCLTech is based in Chennai, India. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
How do I apply for the Senior Technical Specialist position at HCLTech?
Click the "Apply Now" button on this page. You will be redirected to HCLTech's official application portal hosted on successfactors where you can submit your application directly.
When was the Senior Technical Specialist job at HCLTech posted?
This Senior Technical Specialist position at HCLTech was posted on Sep 3, 2026. Apply as soon as possible — early applications are often reviewed first.
Senior Technical Specialist
HCLTech
You'll be redirected to HCLTech's official application page on successfactors.