AI Data Engineer
Job Location:
Monthly Salary:
Posted on:
2 days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
AI Data Engineer
Location:Mumbai & Bangalore Employment Type:Full-Time
Job Description
The Role
As anAI Data Engineerspecializing in MLOps and AIOps you will play a critical role in deploying operating and optimizing enterprise-grade AI solutions built on Azure. You will collaborate closely with AI Developers Product Owners Governance leaders and Cloud Architects to ensure these systems are reliable scalable and cost-effective.
This role goes beyond traditional DevOpsâits about engineering AI into the fabric of enterprise operations enabling secure observable and governed machine learning deployments that deliver measurable business value.
Responsibilities:
Deploy and monitor AI models across Azure services with robust telemetry for performance drift and availability
Manage model upgrades including APIs and UIs with structured rollout version control and rollback support
Optimize performance and cost through testing profiling and tuning of inference infrastructure and pipelines
Implement MLOps pipelines for continuous integration deployment and lifecycle management using Azure ML and GitHub Actions
Ensure compliant change management for all AI-related deployments with auditability security and governance controls
What Were Looking For
Basic Qualifications
Bachelors degree in Computer Science Engineering Data Science or related technical field
3 years of hands-on experience in MLOps and/or AIOps ideally within an Azure cloud environment
Demonstrated expertise with Azure ML Synapse Data Lake App Services Cosmos DB and Azure AI Foundry
Preferred/Desired Qualifications
Consulting background with a strong bias for action
Experience with Workflow Design: Prompt flow automation pipelines and human-in-the-loop systems
Knowledge of Post-Training Techniques: Fine-tuning instruction tuning RLHF and domain adaptation
Proficiency with Azure DevOps App Insights Log Analytics Key Vault and Managed Identity integration
Experience with tools for inference performance testing and profiling (e.g. locust K6 or custom scripts)
Strong understanding of Model Evaluation: Performance metrics benchmark development and A/B testing frameworks
Knowledge of model observability telemetry and incident response for AI systems
Location:Mumbai & Bangalore Employment Type:Full-Time
Job Description
The Role
As anAI Data Engineerspecializing in MLOps and AIOps you will play a critical role in deploying operating and optimizing enterprise-grade AI solutions built on Azure. You will collaborate closely with AI Developers Product Owners Governance leaders and Cloud Architects to ensure these systems are reliable scalable and cost-effective.
This role goes beyond traditional DevOpsâits about engineering AI into the fabric of enterprise operations enabling secure observable and governed machine learning deployments that deliver measurable business value.
Responsibilities:
Deploy and monitor AI models across Azure services with robust telemetry for performance drift and availability
Manage model upgrades including APIs and UIs with structured rollout version control and rollback support
Optimize performance and cost through testing profiling and tuning of inference infrastructure and pipelines
Implement MLOps pipelines for continuous integration deployment and lifecycle management using Azure ML and GitHub Actions
Ensure compliant change management for all AI-related deployments with auditability security and governance controls
What Were Looking For
Basic Qualifications
Bachelors degree in Computer Science Engineering Data Science or related technical field
3 years of hands-on experience in MLOps and/or AIOps ideally within an Azure cloud environment
Demonstrated expertise with Azure ML Synapse Data Lake App Services Cosmos DB and Azure AI Foundry
Preferred/Desired Qualifications
Consulting background with a strong bias for action
Experience with Workflow Design: Prompt flow automation pipelines and human-in-the-loop systems
Knowledge of Post-Training Techniques: Fine-tuning instruction tuning RLHF and domain adaptation
Proficiency with Azure DevOps App Insights Log Analytics Key Vault and Managed Identity integration
Experience with tools for inference performance testing and profiling (e.g. locust K6 or custom scripts)
Strong understanding of Model Evaluation: Performance metrics benchmark development and A/B testing frameworks
Knowledge of model observability telemetry and incident response for AI systems
Required Skills:
MLOpsAIOpsAzure cloud
