Generative AI Developer

Roles and Responsibilities

• Design, develop, and implement enterprise-grade applications leveraging Generative AI and Large Language Models (LLMs).
• Build scalable backend services and microservices using Java and Spring Boot.
• Integrate Azure OpenAI and Azure AI services into enterprise applications.
• Develop and maintain RESTful APIs and services to support AI-powered business applications.
• Implement LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG), and AI-driven workflows.
• Utilize frameworks such as LangChain and LangGraph to develop intelligent applications and orchestrate AI workflows.
• Collaborate with cross-functional teams to identify business requirements and translate them into technical solutions.
• Develop, test, troubleshoot, and optimize AI-enabled applications for performance, scalability, and reliability.
• Deploy and maintain applications within the Microsoft Azure cloud ecosystem.
• Follow software engineering best practices, including CI/CD, code quality, security, and application monitoring.
• Stay updated with emerging Generative AI technologies, frameworks, and cloud-based AI capabilities.

Required Qualifications

• Strong hands-on experience in Java, Spring Boot, Microservices, and REST API development.
• Practical experience in Machine Learning, including developing or implementing ML-based applications or solutions.
• Experience with Generative AI, Large Language Models (LLMs), and AI application development.
• Hands-on experience with Azure OpenAI, Azure AI services, and Microsoft Azure cloud technologies.
• Strong understanding of application architecture, API integration, and distributed systems.
• Experience developing, deploying, and maintaining cloud-based applications.
• Ability to integrate AI capabilities into existing enterprise applications.
• Strong problem-solving, analytical, and communication skills.

Preferred Qualifications

• Hands-on experience developing Generative AI applications using Azure OpenAI and the Azure cloud ecosystem.
• Strong experience with LangChain and LangGraph frameworks.
• Experience implementing Retrieval-Augmented Generation (RAG) solutions.
• Knowledge of prompt engineering, embeddings, vector databases, and semantic search.
• Experience with LLM integration, orchestration, and AI-powered application workflows.
• Experience deploying and managing applications on Microsoft Azure.
• Familiarity with Azure AI Search, Azure Machine Learning, and related Azure AI services.
• Knowledge of containerization, CI/CD pipelines, and cloud-native application development.

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