MLOps Architect - AI Platform & Pipeline Ops
NTT DATA
MLOps Architect - AI Platform & Pipeline Ops
Job Description
"Key Responsibilities:
• Build and maintain ML pipelines for training, validation, deployment, and monitoring
• Implement CI/CD for ML artifacts, including data versioning and model registries
• Monitor model performance and drift; trigger retraining workflows as needed
• Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services
• Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycle
Required Skills:
• Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)
• Proficiency in Python, Bash, Docker, and Kubernetes
• Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation)
• Experience in model monitoring, logging, and alerting
• Bonus: experience in BPS / regulated domains with compliance-aware deployment"
Locations
- Hyderabad, TG, IN
Salary
2,800,000 - 5,500,000 INR / yearly
Source: estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- AWSintermediate
- Azureintermediate
- GCPintermediate
- Cloudintermediate
- Dockerintermediate
- Kubernetesintermediate
- Terraformintermediate
- CI/CDintermediate
- AIintermediate
Required Qualifications
- Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex A (experience)
- Proficiency in Python, Bash, Docker, and Kubernetes (experience)
- Familiarity with cloud infra (AWS/GCP/Azur (experience)
- and IaC (Terraform, CloudFormatio (experience)
- Experience in model monitoring, logging, and alerting (experience)
- Bonus: experience in BPS / regulated domains with compliance-aware deployment" (experience)
Preferred Qualifications
- experience in BPS / regulated domains with compliance-aware deployment" (experience)
Responsibilities
- Build and maintain ML pipelines for training, validation, deployment, and monitoring
- Implement CI/CD for ML artifacts, including data versioning and model registries
- Monitor model performance and drift; trigger retraining workflows as needed
- Manage infrastructure (cloud, containerized, on-pre
- for high-availability AI services
- Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycleRequired
Benefits
- general: Competitive compensation package with performance bonuses
- general: Comprehensive health insurance coverage
- general: 401(k) retirement plan with company matching
- general: Flexible work arrangements and remote work options
- general: Professional development and training programs
- general: Career advancement opportunities in global organization
Tags & Categories
MLOps Architect - AI Platform & Pipeline Ops
NTT DATA
MLOps Architect - AI Platform & Pipeline Ops
Job Description
"Key Responsibilities:
• Build and maintain ML pipelines for training, validation, deployment, and monitoring
• Implement CI/CD for ML artifacts, including data versioning and model registries
• Monitor model performance and drift; trigger retraining workflows as needed
• Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services
• Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycle
Required Skills:
• Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)
• Proficiency in Python, Bash, Docker, and Kubernetes
• Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation)
• Experience in model monitoring, logging, and alerting
• Bonus: experience in BPS / regulated domains with compliance-aware deployment"
Locations
- Hyderabad, TG, IN
Salary
2,800,000 - 5,500,000 INR / yearly
Source: estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- AWSintermediate
- Azureintermediate
- GCPintermediate
- Cloudintermediate
- Dockerintermediate
- Kubernetesintermediate
- Terraformintermediate
- CI/CDintermediate
- AIintermediate
Required Qualifications
- Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex A (experience)
- Proficiency in Python, Bash, Docker, and Kubernetes (experience)
- Familiarity with cloud infra (AWS/GCP/Azur (experience)
- and IaC (Terraform, CloudFormatio (experience)
- Experience in model monitoring, logging, and alerting (experience)
- Bonus: experience in BPS / regulated domains with compliance-aware deployment" (experience)
Preferred Qualifications
- experience in BPS / regulated domains with compliance-aware deployment" (experience)
Responsibilities
- Build and maintain ML pipelines for training, validation, deployment, and monitoring
- Implement CI/CD for ML artifacts, including data versioning and model registries
- Monitor model performance and drift; trigger retraining workflows as needed
- Manage infrastructure (cloud, containerized, on-pre
- for high-availability AI services
- Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycleRequired
Benefits
- general: Competitive compensation package with performance bonuses
- general: Comprehensive health insurance coverage
- general: 401(k) retirement plan with company matching
- general: Flexible work arrangements and remote work options
- general: Professional development and training programs
- general: Career advancement opportunities in global organization
Tags & Categories