Java Architect

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.