Title: Lead Integration Developer
Exp: 9-12 years
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
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Own and lead end-to-end integration solution design and delivery across complex enterprise programs.
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Define and implement integration architectures, API strategies, and reusable frameworks aligned with enterprise standards.
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Create, review, and govern High-Level Designs (HLDs) and Low-Level Designs (LLDs) ensuring scalability, performance, and maintainability.
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Drive architecture governance, technical design reviews, and best practices adoption.
Azure Integration Services Implementation
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Lead development using Azure Logic Apps, Azure Functions, Azure Data Factory, and broader Azure Integration Services.
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Design highly resilient, secure, and scalable workflows and orchestrations.
API Management & Governance
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Design, publish, secure, and monitor APIs via Azure API Management (APIM).
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Drive enterprise API governance, lifecycle management, versioning, and security standards.
Messaging & Event-Driven Architecture
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Build and implement event-driven and asynchronous architectures using Azure Service Bus, Event Grid, and Event Hubs.
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Champion loosely coupled, scalable integration patterns.
Hybrid Integration
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Architect and implement integrations between on-premises systems and cloud platforms, ensuring seamless and reliable connectivity.
AI & Copilot-Driven Engineering
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Leverage Microsoft Copilot (GitHub Copilot, M365 Copilot) to improve developer productivity, accelerate code development, and enhance solution quality.
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Utilize Generative AI for:
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Automated code generation, refactoring, and documentation (LLDs, API specs)
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Intelligent workflow design and optimization
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AI-assisted debugging, testing, and root cause analysis
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Integrate AI-powered services (Azure OpenAI, Cognitive Services) within integration solutions to enable intelligent data processing and decision-making.
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Promote AI-first engineering practices, including prompt engineering and responsible AI usage.
Monitoring, Security & Compliance
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Implement end-to-end observability using Cribl.
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Ensure secure data handling, compliance, and governance across integration solutions.
Technical & Team Leadership
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Provide strong technical leadership and direction across teams.
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Conduct design reviews, code reviews, and enforce engineering standards.
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Mentor team members, enabling skill development in integration, cloud, and AI technologies.
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Manage technical risks, escalations, and critical design decisions.
Collaboration & Stakeholder Management
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Collaborate with cross-functional teams, architects, business stakeholders, and vendors.
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Align technology solutions with business goals and enterprise strategy.
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Build and nurture a high-performing, collaborative, and innovation-driven integration team.
Required Skills
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Experience: 9–12+ years in software engineering with strong focus on cloud and iPaaS integrations.
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Azure Expertise: Deep hands-on experience with Azure Integration Services (AIS).
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Development Skills: Strong proficiency in C#, .NET, REST/SOAP APIs, JSON, XML.
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Design Skills: Extensive experience in LLD/HLD creation, architecture design, and solution documentation.
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API Expertise: Strong experience in API architecture, governance, and lifecycle management.
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Integration Patterns: Expertise in event-driven, microservices, and hybrid integration architectures.
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AI & Copilot Skills: (Good to have)
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Hands-on usage of GitHub Copilot / M365 Copilot for development acceleration
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Understanding of prompt engineering and AI-assisted development workflows
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Exposure to Azure OpenAI / AI services integration
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Delivery Experience: Proven experience leading large-scale enterprise integration delivery.
Preferred Skills
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Experience with MuleSoft architecture / coexistence strategies.
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Exposure to advanced AI/ML integration use cases and intelligent automation.
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Knowledge of data transformation standards (EDI, XML, JSON) and large-scale data pipelines.
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Familiarity with AI governance, ethics, and secure AI adoption in enterprises.
Preferred Certifications
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Azure Solutions Architect Expert (AZ-305)
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Azure Developer Associate (AZ-204)
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DevOps Engineer Expert (AZ-400)
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(Good to have) Azure AI Engineer Associate (AI-102)
