Principal Data Engineer

About the Role

We are seeking a Azure and AI Engineer to serve as the technical build lead for a large-scale agentic automation platform. This role will own the end-to-end solution architecture and engineering execution across Azure, AI/LLM workflows, platform operations, and the reusable framework.

This person will lead the offshore engineering team, define the technical standards, and ensure the platform is scalable, secure, and reusable across multiple workloads.

Key Responsibilities

1) Own the end-to-end technical architecture of the agentic automation platform, including component boundaries, integration patterns, and agent orchestration.

2) Design and implement Azure integration and orchestration using Azure Durable Functions, Logic Apps, Event Hub, Event Grid, and Service Bus.

3) Build the self-managing operations layer covering pipeline monitoring, data freshness checks, self-validation, self-healing, and controlled remediation with audit tracking.

4) Define the safety and decision framework for automated actions, including impact assessment, confidence thresholds, validation, and escalation.

5) Design and implement the config-driven framework so new workloads or rule sets can be onboarded through configuration and mapping changes without modifying the core platform code.

6) Define and manage the Azure infrastructure architecture, including data services, security, RBAC, Key Vault, IaC, and CI/CD.

7) Lead and review offshore engineering work, ensuring the agreed architecture, coding standards, and framework patterns are followed across workstreams.

8) Prepare solution design documents, architecture blueprints, technical documentation, and operational runbooks.

Required Skills & Experience

1) 10+ years of experience in Azure/cloud engineering.

2) Strong hands-on experience with Azure integration and data services, including Azure Durable Functions, Logic Apps, Event Hub, Event Grid, Azure Functions, Service Bus, Microsoft Fabric, Azure Synapse, Azure Data Lake, and Azure SQL.

3) Proven experience building AI/LLM-based applications or agentic solutions, including multi-agent workflows and tool integration.

4) Good understanding of LLM concepts such as structured outputs, schema-based prompting, response validation, and confidence-based processing.

5) Strong Python and SQL skills for platform engineering, data pipelines, and AI integration.

6) Experience with Azure Monitor, Power BI, or similar tools for monitoring and reporting is an advantage.

7) Strong technical leadership and communication skills, with the ability to work with both engineering teams and business stakeholders.