Job Duties and Responsibilities:

• Ensure full and accurate adoption of modern software engineering and delivery practices, including Twelve-Factor (12-Factor) methodology, Agile, BDD, Stateless Design, and Java REST/Spring Boot-based Microservices integrated with Generative AI agents.
• Bring deep knowledge and experience in designing and implementing scalable solutions in the Cloud (AWS, OpenShift, Azure, AI Platform), specifically focusing on high-concurrency LLM (Large Language Model) orchestration and Vector Database deployment.
• Design frameworks for handling dynamic, use-case-specific prompts; oversee the fine-tuning of GPT and other LLMs to align with client-specific summarization and data insight requirements.
• Implement advanced Retrieval-Augmented Generation (RAG) methodologies; optimize vectorization techniques (Cosine Similarity, Dot Product) and design scalable indexing pipelines for large-scale data retrieval.
• Manage APIGEE Edge On-Premises and Hybrid infrastructures; handle customization and integration of AI endpoints, ensuring rate limiting and security for LLM token consumption.
• Oversee APIGEE Portal customization NodeJS, and JavaScript to facilitate developer access to AI-driven API services.
• Deploy APIGEE, Kong API Gateway and develop custom policies in NodeJS / JavaScript for prompt validation, sensitive data masking, and AI-pipeline debugging.
• Design and develop security policies and custom integration with IAM Systems (Okta, Azure AD, PingID) to protect AI assets and data pipelines.
• Build secure integrations with Single Page Apps and Mobile Apps using OAuth2, SSO, OpenID Connect, and JWT for AI-driven visualization platforms.
• Enable complex call patterns for Intranet and Internet access to physical services and AI sandbox environments via virtual endpoints published to APIGEE.
• Ensure all legacy security models for AI and Backend APIs have a defined migration path or counterpart within the APIGEE ecosystem.
• Ensure the CI/CD pipeline covers the full lifecycle management for enterprise AI use cases, including model versioning, deployment of Spring Boot microservices, and Kafka-based data streams.
• Manage relationships with key technology and business partners to derive actionable insights from summarized AI data to aid executive decision-making.

Technologies involved:

Java/J2EE (Java 17), Generative AI (GPT-4, Azure OpenAI, Ollama, Anthropic Claude, LangChain),VectorDatabases (OpenSearch, Pinecone), Oracle, APIGEE Edge / Hybrid, Spring / SpringBoot, Kafka / ActiveMQ, Python (AI Integration), RHDM (Drools), Microservices, AWS (EKS, Lambda, SageMaker), Docker/Kubernetes, Swagger/OpenAPI 3.x, BDD, Python ,Redis, New Relic, Splunk.