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Overview
We are seeking an experienced and highly motivated Lead Java Developer to drive the design, development, and delivery of scalable, cloud-native applications. This role combines deep technical expertise in Java development with leadership responsibilities, including mentoring teams, driving architectural decisions, and integrating AI-driven solutions within modern cloud environments.
Key Responsibilities
Lead the end-to-end design, development, and deployment of enterprise-grade Java applications
Architect and implement scalable, secure, and high-performing solutions preferably on
Azure Cloud Drive the integration of AI/ML capabilities (e.g., APIs, models, automation) into business applications
Collaborate with cross-functional stakeholders to translate business requirements into technical solutions
Provide technical leadership, mentorship, and guidance to a team of developers
Establish and enforce coding standards, best practices, and development frameworks
Oversee code reviews, performance optimization, and system troubleshooting
Manage project timelines, delivery commitments, and resource planning
Ensure CI/CD pipelines, DevOps practices, and cloud-native development standards are followed
Stay updated with emerging technologies, particularly in AI, cloud computing, and microservices architecture
Required Qualifications
Bachelor's degree in Computer Science, Engineering, or related field
5-7+ years of experience in Java development, with at least 2–3 years in a leadership role
Strong expertise in Java (Spring Boot, Microservices architecture)
Familiarity or hands-on experience with Microsoft Azure (App Services, Azure Functions,
AKS, Azure DevOps)
Experience integrating AI/ML services (e.g., REST APIs, Azure AI, OpenAI, or similar platforms)
Solid understanding of RESTful APIs, distributed systems, and event-driven architecture
Experience with relational and NoSQL databases
Proven experience in leading development teams and delivering complex projects
Preferred Qualifications
Experience with AI frameworks or tools (e.g., TensorFlow, PyTorch, or cloud-based AI services)
Familiarity with containerization (Docker, Kubernetes)
Exposure to data engineering or big data technologies
Strong knowledge of DevOps practices and CI/CD pipelines
