Job Description:
Department Overview
The Ad Product Section (APS) division is a high-performance, global team distributed across Tokyo, Singapore, India, and China. We pride ourselves on being a hub of technical excellence, where motivated, diverse, and talented engineers collaborate to solve complex problems at scale.
We are seeking a Senior Software Engineer to join our team and help us define the next generation of our advertising platform. This is not just a coding role; it is a position of architectural and technical cultural leadership.
You will own the end-to-end product development lifecycle, leveraging a modern tech stack and AI-augmented workflows to deliver high-impact solutions. We are looking for a technologist who doesn't just keep up with the industry, but shapes it—someone who thrives on building scalable systems, mentoring the next generation of engineers, and integrating AI to push the boundaries of productivity and software quality.
If you are a builder who values technical rigor, thrives in cross-functional environments, and is eager to contribute to Rakuten’s expansive global ecosystem, we want to talk to you.
Position:
Position Details
Roles and Responsibilities
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Project Execution & Engineering Excellence
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Technical Leadership: Translate complex business requirements into robust, scalable, and maintainable technical specifications and architectural designs.
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Lifecycle Ownership: Own the full SDLC—from initial design and implementation to deployment, monitoring, and proactive maintenance across all environments.
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Quality Assurance: Conduct rigorous code reviews, ensuring adherence to design patterns, security best practices, and long-term maintainability.
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AI-Augmented Development: Drive engineering velocity by integrating AI-powered coding assistants (e.g., GitHub Copilot, Cursor) into the team’s workflow. Establish standards for AI-assisted code generation, ensuring that AI-written code is rigorously vetted for security, accuracy, and alignment with system architecture.
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Strategic Innovation & Productivity
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System Stewardship: Maintain a deep understanding of the product ecosystem to identify technical debt and architectural bottlenecks. Proactively propose and lead initiatives to refactor or enhance systems for better performance and scalability.
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AI-Driven Productivity: Leverage advanced prompt engineering to automate repetitive tasks—such as documentation generation, legacy code refactoring, and test suite creation—to maximize team bandwidth.
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Tech Radar: Stay ahead of the curve by evaluating emerging technologies and AI tools, creating strategic roadmaps to integrate innovations that provide a competitive advantage.
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Collaboration & Cross-Functional Impact
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Partnership: Act as the primary technical bridge between Product, Design, and Engineering. Translate high-level business objectives into clear, actionable technical plans.
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Technical Advocacy: Influence product roadmaps by anticipating future technical constraints and opportunities, ensuring the platform remains resilient as business needs evolve.
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Mentorship & Technical Culture
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Team Growth: Actively mentor junior engineers and interns. Foster a culture of technical excellence by providing constructive feedback, guiding career progression, and promoting best practices in software design.
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Knowledge Sharing: Champion a culture of continuous learning, ensuring the team stays informed on modern development paradigms and AI-assisted workflows.
Work Environment
Mandatory Qualifications:
Core Requirements (The "Must-Haves")
- Experience & Impact: 10+ years of professional software engineering experience, including 3+ years in principal/architect-level role and a proven track record of leading complex, large-scale web application projects from inception to production.
- Architectural Mastery: Deep hands-on expertise in Java (Spring Boot/Batch) and Cloud-native architectures (GCP ecosystem: BigQuery, DataProc, Composer). Ability to design for scale, performance, and high availability.
- Data Strategy: Strong proficiency in both relational (MariaDB/MySQL) and NoSQL (MongoDB) databases, with the ability to choose the right data storage strategy for specific architectural needs.
- AI-Augmented Engineering:
- Agentic AI: Demonstrated experience in designing or implementing Agentic AI workflows (e.g., using frameworks like LangChain, n8n, dify, or custom agent orchestration) to solve complex business problems.
- LLM Systems Architecture & Design: Expert in designing and deploying production-grade LLM applications. Proficient in end-to-end architectural strategy, including model selection, optimized RAG pipelines, agentic orchestration, tool integration, and rigorous evaluation frameworks to ensure performance and reliability..
- Developer Productivity: Expert-level fluency in AI-assisted coding tools (Cursor, GitHub Copilot, Claude Code) to drive high-velocity development while maintaining rigorous security and quality gates.
- System Design:
- Analyzing complex business systems, industry requirements and governance, and creating solution designs from them
- Proven architecture and design experience for cloud-based platforms and enterprise applications including defining microservices boundaries, API design, and asynchronous communication patterns.
- Soft Skills & Ownership: A "get-things-done" mindset with demonstrated accountability for system health and team output. Excellent communication skills, with the ability to present architectural decisions to mixed audiences including Senior Management, Product Management, and Engineers across disciplines.
- Product Thinking with Analytical Capabilities: Strategic Product Thinking with deep analytical abilities to transform complex business requirements into high-margin, AI-driven platform capabilities.
Desired Qualifications:
Technical Proficiencies
- Languages & Frameworks: Java (Spring Boot/Batch), JavaScript/TypeScript (HTML5/CSS3)
- Data & Infrastructure: MariaDB/MySQL, MongoDB, GCP (BigQuery, DataProc, Composer)
- AI/Agent Frameworks: LangChain, LangGraph, n8n, ADK, MCP (Model Context Protocol), or equivalent
- LLM Tooling: Prompt engineering, RAG, vector databases, LLM observability (LangFuse or equivalent)
- AI Developer Tools: Claude Code, Cursor, GitHub Copilot — fluent in AI-assisted engineering workflows
- Tools & Process: Version Control (Git), Agile/Scrum, Task Management (Jira)
Preferred Qualifications (The "Nice-to-Haves")
- Big Data Ecosystem: Experience with Hadoop, Spark, Hive, Tez, or Presto.
- Database: Dimensional database (e.g. ClickHouse)
Frontend Expertise: Proficiency in modern frameworks like React.js or Angular.
- Caching & Messaging: Experience with Redis, Couchbase, Cassandra, Kafka, or RabbitMQ.
- DevOps & Quality: Hands-on experience with CI/CD pipelines (Jenkins), Test Automation (JUnit), and Code Quality tooling (SonarQube).
- Domain Expertise: Prior experience in AdTech or high-traffic advertising system architectures is a significant plus.
- Data Visualization: Familiarity with BI tools (MicroStrategy, Tableau) for data-driven decision-making.
- AI-adoption: Experience evaluating and governing AI tool adoption across engineering teams.
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