Azure Integration Developer

Role Summary

A strategic and hands-on Integration Leader responsible for driving end-to-end delivery of enterprise integration solutions. Ensures alignment with enterprise architecture, leads technical design (HLD/LLD), and builds high-performing teams while leveraging modern cloud, AI, and Copilot-enabled development capabilities.

Key Responsibilities

Integration Architecture & Delivery

• Own and lead end-to-end integration solution design and delivery across complex enterprise programs.

• Define and implement integration architectures, API strategies, and reusable frameworks aligned with enterprise standards.

• Create, review, and govern High-Level Designs (HLDs) and Low-Level Designs (LLDs) ensuring scalability, performance, and maintainability.

• Drive architecture governance, technical design reviews, and best practices adoption.

Azure Integration Services Implementation

• Lead development using Azure Logic Apps, Azure Functions, Azure Data Factory, and broader Azure Integration Services.

• Design highly resilient, secure, and scalable workflows and orchestrations.

API Management & Governance

• Design, publish, secure, and monitor APIs via Azure API Management (APIM).

• Drive enterprise API governance, lifecycle management, versioning, and security standards.

Messaging & Event-Driven Architecture

• Build and implement event-driven and asynchronous architectures using Azure Service Bus, Event Grid, and Event Hubs.

• Champion loosely coupled, scalable integration patterns.

Hybrid Integration

• Architect and implement integrations between on-premises systems and cloud platforms, ensuring seamless and reliable connectivity.

AI & Copilot-Driven Engineering

• Leverage Microsoft Copilot (GitHub Copilot, M365 Copilot) to improve developer productivity, accelerate code development, and enhance solution quality.

• Utilize Generative AI for:

• Automated code generation, refactoring, and documentation (LLDs, API specs)

• Intelligent workflow design and optimization

• AI-assisted debugging, testing, and root cause analysis

• Integrate AI-powered services (Azure OpenAI, Cognitive Services) within integration solutions to enable intelligent data processing and decision-making.

• Promote AI-first engineering practices, including prompt engineering and responsible AI usage.

Monitoring, Security & Compliance

• Implement end-to-end observability using Cribl.

• Ensure secure data handling, compliance, and governance across integration solutions.

Technical & Team Leadership

• Provide strong technical leadership and direction across teams.

• Conduct design reviews, code reviews, and enforce engineering standards.

• Mentor team members, enabling skill development in integration, cloud, and AI technologies.

• Manage technical risks, escalations, and critical design decisions.

Collaboration & Stakeholder Management

• Collaborate with cross-functional teams, architects, business stakeholders, and vendors.

• Align technology solutions with business goals and enterprise strategy.

• Build and nurture a high-performing, collaborative, and innovation-driven integration team.

Required Skills

• Experience: 9–12+ years in software engineering with strong focus on cloud and iPaaS integrations.

• Azure Expertise: Deep hands-on experience with Azure Integration Services (AIS).

• Development Skills: Strong proficiency in C#, .NET, REST/SOAP APIs, JSON, XML.

• Design Skills: Extensive experience in LLD/HLD creation, architecture design, and solution documentation.

• API Expertise: Strong experience in API architecture, governance, and lifecycle management.

• Integration Patterns: Expertise in event-driven, microservices, and hybrid integration architectures.

• AI & Copilot Skills: (Good to have)

• Hands-on usage of GitHub Copilot / M365 Copilot for development acceleration

• Understanding of prompt engineering and AI-assisted development workflows

• Exposure to Azure OpenAI / AI services integration

• Delivery Experience: Proven experience leading large-scale enterprise integration delivery.

Preferred Skills

• Experience with MuleSoft architecture / coexistence strategies.

• Exposure to advanced AI/ML integration use cases and intelligent automation.

• Knowledge of data transformation standards (EDI, XML, JSON) and large-scale data pipelines.

• Familiarity with AI governance, ethics, and secure AI adoption in enterprises.

Preferred Certifications

• Azure Solutions Architect Expert (AZ-305)

• Azure Developer Associate (AZ-204)

• DevOps Engineer Expert (AZ-400)

• (Good to have) Azure AI Engineer Associate (AI-102)