Full Stack Engineer (experience in AI)

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.