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Senior Backend Engineer, ML Platform, Tokyo

マネーフォワードJapanバックエンドソフトウェアエンジニア
API GatewayAWSCloudWatchDatabricksDockerECSFargateFastAPIGitHubGitHub ActionsOpenAPIPythonRESTS3Terraform

業務内容

  • Design and implement a high-performance credit microservice (private REST API) to support the FY2027 Digital Bank launch.
  • Design API contracts (OpenAPI), versioning, backward compatibility, and align with Digital Bank and group stakeholders.
  • Productionize models and credit logic: SageMaker endpoints integration, latency budgeting, timeout/retry/circuit-breaker, and fallback design.
  • Implement credit domain logic with correctness, explainability, idempotency, reproducibility, and audit trails.
  • Optimize data supply: efficient data loading from object storage, freshness management, caching, and asynchronous/parallel model calls.
  • Own quality, testing, and instrumentation: unit/contract/load testing, regression processes, structured logging, traceability, and SLI-driven instrumentation.
  • Lead technically: establish standards, code reviews, and drive development process improvements.

技術スタック

必須スキル

  • 5+ years building and operating web APIs/microservices in Python or similar
  • Experience with high-traffic, low-latency APIs and performance tuning
  • API contract design, error taxonomy, timeouts, retries, and fallbacks for external integrations
  • Strong testing/QA background with automated testing in the development process
  • AWS cloud development with containers and CI/CD

歓迎スキル(該当する場合)

  • ML integration/MLOps, calling models on SageMaker and designing for inference latency
  • Data platforms: S3, Glue, Databricks; inform technical specs and incident analysis
  • Terraform/IaC for infrastructure changes via PRs
  • Finance domain exposure (credit/payments), with auditability and advanced security (mTLS, OAuth, JWT)
  • Leadership経験(Tech Lead/Scrum Master) with architecture and process standardization

キャリア成長観点

  • Digital Bankローンチを見据えたクレジットAPIの全体責任を通じ、アーキテクチャと品質標準を牽引できる成長機会
  • SRE・MLエンジニア・データ基盤等と横断的に協働し、技術決定を推進する影響力の強化
  • MLモデル統合・MLOpsやレイテンシ予算設計など、AI時代の基盤技術スキルを実務で深化
  • 大規模金融サービスの信頼性・セキュリティ・監査性の経験を積み、Terraform・CI/CD等の運用スキルを拡張
  • 技術リーダーシップや標準化推進の機会を通じて、将来の上位リーダー職・アーキテクト職への道を開く

データ取得日: 2026/9/10

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