The Job

Senior Technical Specialist

Technology, Data & Digital · Data, AI & Analytics · Data Science · DevOps

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In short

We are seeking a Senior Technical Specialist in Chennai, India, to leverage Vertex AI, MLOps, and GCP technologies. The role involves building and managing ML workflows, data pipelines, and ensuring cloud architecture security.

Responsibilities

  • Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring.
  • Understanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems.
  • Building reproducible and automated ML workflows is a key MLOps capability.
  • Containerized ML workflows are a core MLOps practice.
  • Detecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems.
  • Reusable, governed features improve model quality and consistency.
  • Deployment automation helps deliver repeatable ML environments and releases.
  • Understanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.

Requirements

  • Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring.
  • MLOps & ML Lifecycle Management
  • Python & ML Frameworks
  • Data Engineering on GCP
  • Vertex AI Pipelines / Kubeflow
  • Containerization & Kubernetes
  • Model Monitoring & Observability
  • Feature Engineering & Feature Stores
  • CI/CD and Infrastructure as Code
  • Cloud Architecture & Security

Desired Qualifications

  • Core GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring.
  • Understanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems.
  • Strong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development.
  • Knowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.
  • Building reproducible and automated ML workflows is a key MLOps capability.
  • Docker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads.
  • Detecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems.
  • Reusable, governed features improve model quality and consistency.
  • Terraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.
  • Understanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.

Benefits

  • At HCLTech, you'll supercharge your potential.
  • You'll find your career.
  • And you'll find your spark.
  • All at a place that knows that helping its customers stay on top starts by putting its people first.
#MLOps#GCP#Vertex AI#Data Engineering#Kubernetes#Python#TensorFlow#PyTorch#Scikit-Learn#BigQuery#Dataflow#Pub/Sub#Cloud Storage#Vertex AI Pipelines#Kubeflow#Docker#Google Kubernetes Engine#Model Monitoring#Feature Engineering#Feature Stores#CI/CD#Infrastructure as Code#Terraform#Cloud Build#GitHub Actions#Cloud Architecture#Security#IAM
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Company

HCLTech

Job Posted

2 weeks ago

Employment Type

Full Time

WorkMode

On Site

Experience Level

Senior

Locations

Chennai, India

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