Java Backend Engineer – AI

About the role

Build scalable backend applications and integrate AI-powered workflows into complex financial services environments.

The Role

At Capco, we’re looking for a Java Backend Engineer with practical experience building AI-powered applications, workflows, and automation solutions within complex financial services environments.

This role combines strong Java backend engineering with hands-on AI development. You’ll build scalable backend services, integrate Large Language Models (LLMs) and AI capabilities into enterprise applications, and develop intelligent workflows that solve business problems and improve operational efficiency.

You’ll also use modern AI-assisted development tools to enhance engineering productivity while maintaining responsibility for code quality, security, and reliability. Working closely with business stakeholders, architects, and technology teams, you’ll help turn evolving requirements into practical, secure, and maintainable solutions.

What You’ll Do

- Design, develop, test, and maintain scalable backend applications and microservices using Java and Spring Boot.

- Build AI-powered applications, workflows, and automation solutions using LLMs, AI APIs, and capabilities such as structured outputs and tool calling.

- Develop REST APIs and integrate AI capabilities with enterprise applications, data sources, and business processes.

- Use AI-assisted development tools such as GitHub Copilot, Cursor, Devin, or equivalent platforms to accelerate coding, testing, debugging, and delivery while maintaining engineering standards.

- Own technical deliverables from design through implementation, testing, deployment, and production support, collaborating with business and technology stakeholders to resolve complex problems.

What We’re Looking For

- 4–6 years of hands-on software development experience, with strong proficiency in Java and backend application development.

- Solid experience with Spring Boot, REST APIs, microservices, system integrations, and modern software engineering practices.

- Practical experience building AI-enabled applications or workflows using LLM APIs, prompt engineering, structured outputs, or tool/function calling, beyond using AI coding assistants alone.

- Experience using AI-assisted development tools such as GitHub Copilot, Cursor, Devin, or equivalent, with the ability to review, validate, test, and securely maintain AI-generated code.

- Experience with automated testing, CI/CD, version control, troubleshooting, and end-to-end delivery, with an understanding of AI evaluation, output validation, guardrails, privacy, and security considerations.

Bonus Points For

- Experience with Python for AI development, automation, or supporting backend services.

- Familiarity with retrieval-augmented generation (RAG), embeddings, vector databases, or hybrid search.