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
- 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)
