Designs and builds end-to-end AI-powered enterprise applications for capital/financial markets, combining Java/Spring Boot microservices and modern frontends with GenAI, Agentic AI, LLM and RAG integration, deployed via Docker/Kubernetes on cloud. Aimed at full-stack engineers with 8-15 years' experience and strong financial-markets domain knowledge.
We are seeking an experienced
Full Stack AI Engineer
with strong
Java development expertise
and deep
Capital Markets / Financial Markets domain knowledge
to design, build, and operationalize AI-powered business solutions.
Responsibilities
Design and develop end-to-end AI-powered solutions using modern GenAI and Agentic AI frameworks.
Build scalable full-stack applications using Java, Microservices, and modern frontend technologies.
Develop proof-of-concepts, prototypes, and MVPs to validate business use cases.
Integrate Large Language Models (LLMs), AI agents, and Retrieval-Augmented Generation (RAG) capabilities into enterprise applications.
Partner with Architecture, Engineering, and DevOps teams to deploy solutions into production environments.
Present solution recommendations and technical approaches to senior stakeholders, including Directors and Managing Directors.
Participate in architecture reviews, technical governance discussions, and innovation initiatives.
Evaluate emerging AI technologies and recommend suitable adoption frameworks.
Contribute to the organization's AI engineering and digital transformation strategy.
Profile
Bachelor's degree in IT/Computer Science or other relevant discipline
Experience: 8 to 15 years in Full Stack Development: Strong background in building end-to-end enterprise applications and integrations.
Domain Knowledge: Strong experience in Capital Markets or Financial Markets products.
Strong hands-on experience in: Java (Java 8/11/17+), Spring Boot, Spring Cloud, Microservices Architecture, RESTful APIs, Enterprise Application Integration
Experience with frontend technologies: Angular, React, or TypeScript, HTML5, CSS3, JavaScript
AI Expertise: Hands-on experience with Agentic AI tools such as Dify, Google ADK, LangChain/LangGraph, Prompt Engineering Frameworks, and AI Skills Engineering.
Business Engagement: Work closely with business users to understand requirements, conduct discovery sessions, and translate business problems into AI solutions.
Solution Design: Design, develop, and demonstrate AI-powered products and prototypes for stakeholder approval.
Data Engineering: Experience with ETL, data pipelines, data integration, and managing structured/unstructured data.
Cloud & DevOps: Hands-on experience with Docker, Kubernetes, OpenShift, PCF, AWS, and CI/CD practices.
Thought Leadership: Strong end-to-end solutioning capability and awareness of emerging AI technologies.