AI Solution Architect

We are seeking an experienced AI Solution Architect to lead the design and implementation of advanced artificial intelligence systems within a complex financial ecosystem. You will bridge the gap between traditional Java-based enterprise architecture and modern generative AI frameworks. This role focuses on building robust, scalable, and secure AI-driven applications that solve critical business challenges.

Our client is a leading global professional services firm operating at the intersection of finance, accounting, and technology. They are committed to driving digital transformation for their clients by leveraging cutting-edge innovation and scalable enterprise solutions. The organization fosters a collaborative culture that values continuous learning and professional growth.

Responsibilities

• Design and architect scalable AI solutions using Java and Spring Boot frameworks.
• Lead the development of GenAI and LLM-based applications, including Agentic AI and RAG architectures.
• Implement advanced retrieval strategies such as Graph and Hybrid Retrieval to enhance model performance.
• Oversee the end-to-end lifecycle of AI systems from design to production deployment.
• Optimize system performance through effective caching strategies and distributed systems design.
• Collaborate with cross-functional teams to integrate AI capabilities into existing financial platforms.
• Ensure high availability and reliability of services using Kubernetes and CI/CD pipelines.
• Mentor engineering teams on best practices for system design, data structures, and algorithms.

Requirements

• 8-12 years of professional experience in software engineering and system architecture.
• Expert-level proficiency in Java and the Spring Boot ecosystem.
• Strong understanding of Microservices architecture and distributed systems.
• Hands-on experience with GenAI, LLMs, Agentic AI, and RAG implementation.
• Proficiency in database management, including SQL and graph-based data structures.
• Solid experience with cloud-native technologies, Kubernetes, and CI/CD workflows.
• Deep knowledge of Data Structures and Algorithms (DSA) for high-performance computing.
• Proven ability to manage production-grade deployments in a hybrid work environment.