Job DescriptionSenior Technical SpecialistChennai, Tamil NaduJob SummarySkillWhy It MattersVertex AICore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.MLOps & ML Lifecycle ManagementUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. Python & ML FrameworksStrong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development.Data Engineering on GCPKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.Vertex AI Pipelines / KubeflowBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. Containerization & KubernetesDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. Model Monitoring & ObservabilityDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. Feature Engineering & Feature StoresReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.CI/CD and Infrastructure as CodeTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.Cloud Architecture & SecurityUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.Key ResponsibilitiesSkillWhy It MattersVertex AICore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.MLOps & ML Lifecycle ManagementUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. Python & ML FrameworksStrong Python skills plus TensorFlow, PyTorch,



Scikit-Learn, and related libraries remain the foundation for model development.Data Engineering on GCPKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.Vertex AI Pipelines / KubeflowBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. Containerization & KubernetesDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. Model Monitoring & ObservabilityDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. Feature Engineering & Feature StoresReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.CI/CD and Infrastructure as CodeTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.Cloud Architecture & SecurityUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.Skill RequirementsSkillWhy It MattersVertex AICore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.MLOps & ML Lifecycle ManagementUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems. Python & ML FrameworksStrong Python skills plus TensorFlow, PyTorch, Scikit-Learn,



and related libraries remain the foundation for model development.Data Engineering on GCPKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.Vertex AI Pipelines / KubeflowBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform. Containerization & KubernetesDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice. Model Monitoring & ObservabilityDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems. Feature Engineering & Feature StoresReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.CI/CD and Infrastructure as CodeTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.Cloud Architecture & SecurityUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.Other Requirements Apply now Information at a GlanceWhy HCLTech?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.HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026 totaled $14.8 billion.

📌 Architect - Ax And (Chennai)
🏢 Assa Abloy
📍 Chennai