Senior Backend Engineer, ML Platform

About this role

This senior backend role owns the application layer of a private credit microservice used by Digital Bank and other group fintech products. The service combines aggregated data from Databricks, machine learning model endpoints on Amazon SageMaker, and in-service credit calculation logic to provide dependable credit information to product teams. The role focuses on turning uncertain model output into deterministic API behavior for the FY2027 Digital Bank launch.

Responsibilities include designing and implementing a high-performance, low-latency microservice in Python and FastAPI; defining service boundaries and data ownership; and designing OpenAPI contracts, error taxonomies, versioning, backward compatibility, and contract-change processes with stakeholders. The engineer will integrate SageMaker endpoints, allocate latency budgets, and implement timeouts, retries, circuit breakers, and fallback behavior. Other duties include implementing credit-limit calculations with boundary and failure cases, idempotency, reproducibility, input and model-version traceability, and audit-trail design.

The role also covers loading aggregated data from object storage, freshness management, consistent cutover, caching, and asynchronous or parallel model calls. Quality ownership includes unit, contract, load, and regression testing for model swaps, structured logging, traceability, and SLI-driven instrumentation. The engineer will lead architecture selection, code reviews, development standards, test strategy, and process improvement within a small scrum team, working closely with site reliability or infrastructure engineers, machine learning engineers, and external stakeholders.

Required qualifications include at least five years of professional software engineering experience designing, building, and operating web APIs and microservices in Python or comparable languages; experience with high-traffic, low-latency applications and application-level performance tuning; external-system integration contracts and failure design; automated testing and quality assurance; AWS cloud application development using containers and CI/CD; and the ability to build agreement across technical roles.

Preferred experience includes machine learning integration and MLOps, Amazon SageMaker inference latency, data platforms such as S3, Glue, and Databricks, Terraform-based infrastructure as code, and financial services, credit, or payments. Experience with auditability, traceability, mTLS, OAuth, or JWT is also preferred, as is leadership as a tech lead or scrum master.

The stated technology stack includes Python, FastAPI, Amazon API Gateway, AWS ECS Fargate, S3, CloudWatch, Amazon SageMaker, GitHub Actions, Docker, Terraform, Databricks, AWS Glue, Claude Code and similar AI development tools, Slack, and Notion.

This is a full-time position in Tokyo with a three-month probation period. The role uses a discretionary labor system for professional work. Basic working hours are 9:30–18:30 with a 60-minute break, while employees may choose working hours at their discretion; overtime may occur. The hybrid policy requires office work at least two days per week as standard, with team office days varying by team. The annual salary range is 7,008,000–11,004,000 JPY, including fixed allowances, and a high-performance bonus may be paid based on semiannual evaluations. Benefits include social insurance, housing and moving allowances for eligible neighborhood arrangements, health checks, influenza vaccination, book-purchase support, a defined-contribution pension, an employee stock ownership plan, and preferential access to certain contracted services. Applicants must be residing in Japan. Business-level Japanese equivalent to JLPT N2 or above and basic business-level English equivalent to TOEIC 700 or above are required, and interviews are conducted in Japanese.