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.
