Java Architect AI GenAI Solutions

Job description

**Role & responsibilities** We are seeking a highly experienced **Java Architect with AI/GenAI** **expertise** to design and deliver next-generation enterprise applications leveraging Java microservices, cloud-native architecture, and Generative AI technologies. The candidate will drive architecture decisions, define technology roadmaps, and build scalable AI-enabled solutions for enterprise customers. **Key Responsibilities:** **Architecture & Design** - Define end-to-end solution architecture for enterprise-scale applications. - Design microservices-based, event-driven, and API-first architectures. - Lead architecture reviews, technology selection, and governance. - Ensure scalability, security, resiliency, and performance of platforms. **Java Technology Leadership** - Architect solutions using Java, Spring Boot, Spring Cloud, REST APIs, Kafka, and distributed systems. - Drive cloud-native adoption using Azure/AWS. - Establish engineering standards, CI/CD practices, and DevSecOps controls. - Mentor development teams and provide technical leadership. **AI / GenAI Responsibilities** - Design and implement GenAI-powered enterprise applications. - Build and deploy RAG (Retrieval Augmented Generation) based solutions. - Architect AI agents using LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks. - Integrate LLMs such as Azure OpenAI, OpenAI, Anthropic, or AWS Bedrock. - Design Vector Database architecture using Pinecone, ChromaDB, FAISS, or Azure AI Search. - Define prompt engineering strategies, evaluation frameworks, and AI guardrails. - Drive responsible AI, security, observability, and governance practices. **Mandatory Skills** **Core Architecture Skills** - Java 17+ - Spring Boot / Spring Cloud - Microservices Architecture - REST APIs & API Gateway - Kafka / Event Streaming - Distributed System Design - Design Patterns & Enterprise Architecture - Docker & Kubernetes/OpenShift - CI/CD (Jenkins, GitHub Actions, Azure DevOps) **AI / GenAI Skills** - LLM Architecture - RAG Frameworks - Prompt Engineering - LangChain / LangGraph - CrewAI / AutoGen - Vector Databases (FAISS, Pinecone, Chroma) - AI Agent Architecture - Azure OpenAI / AWS Bedrock **Cloud** - Azure (Preferred) or AWS - Kubernetes - Serverless Architecture - Monitoring & Observability **Preferred Skills** - Python for AI integrations. - Knowledge of AI governance and model lifecycle management. - Experience with BFSI domain. - Exposure to multi-agent systems and autonomous workflows.