AI Platform Engineer

About PayPay Card

PayPay Card Corporation was established in 2021 to provide users a FinTech service that is more accessible and convenient compared to previous credit cards and credit services, by integrating with the PayPay payment platform, which has surpassed 70 million users since its launch (as of July 2025).

We are looking for people who are passionate about refining our products at an overwhelming speed that other companies cannot match, as well as professionals who are interested in promoting the spread of cashless payments in Japan and the use of these payments as a financial life platform. Let us work together to create new value for users.

※ Please note that you cannot apply or be selected in parallel with PayPay Corporation, PayPay Card Corporation and PayPay Securities Corporation.

Job Description

PayPay Card is looking for an AI Platform Engineer focused on cloud-native GenAI infrastructure and enablement. This role will build and operate the foundation that enables internal teams to deliver and operate GenAI applications, agents, RAG systems, and related AI workloads reliably, safely, and cost-effectively

Responsibilities

Architect and build AI platform capabilities for applications, agents, RAG systems and related AI workloads

Architect and build infrastructure that is easy to maintain, update and improve

Architect and build infrastructure with appropriate reliability and recovery capabilities for internal AI platform services

Work together with our Security Engineers to provision secure and governed AI platform infrastructure

Build and maintain deployment automation to ensure fast delivery of AI platform services to our developers

Provide self-service capabilities and standard deployment patterns for developers to easily deploy and operate AI-powered application infrastructure

Build and maintain reusable platform templates, deployment patterns and integrations for GenAI applications, agents, RAG systems, MCP-based integrations and agent-to-agent workflows

Build and support monitoring and evaluation capabilities for GenAI systems, including usage, cost, reliability, agent execution and adoption metrics

Continuously research, evaluate, and prototype emerging AI trends, frameworks, and open-source tools to ensure the platform remains cutting-edge.

Drive R&D initiatives for new AI platform capabilities, keeping pace with the rapid evolution of agentic workflows and LLM infrastructure.

Tech Stack

AWS: Bedrock, Bedrock Knowledge Bases, OpenSearch, Neptune, S3, ECS, EKS, Lambda, CloudWatch, Cognito, SQS, KMS, Secrets Manager, MSK, CodeCommit, CodeBuild, CodeDeploy, CodePipeline, CloudFormation and other services

AI platform / GenAI capabilities: RAG, vector stores, graph databases, model access patterns, MCP-based integrations, agent orchestration, agent-to-agent workflows, evaluation and observability tooling

Terraform, GitHub Actions, Prometheus, Grafana, Dynatrace, Atlantis, ArgoCD, OpenTelemetry

Required Qualifications

More than 5 years of technical experience in cloud-based infrastructure platforms

Ability to demonstrate high degree of ownership in a Production environment

Good understanding of cloud security best practices and payment industry compliance standards

Experience designing, building and operating cloud platform capabilities for internal developers

Experience with cloud infrastructure and platform systems availability, performance and cost management

Extensive technical hands-on experience with compute, storage and analytics services on cloud platforms

Experience with IaC tools such as Terraform, CloudFormation, CDK

Experience with cloud services monitoring, detection and response

Experience with cloud services performance tuning, cost controls and management

Experience in cloud infrastructure service patching and upgrades

Familiarity with AI platform concepts such as GenAI applications, agents, RAG systems, vector stores, model access patterns and evaluation/observability capabilities

PayPay DevOps emphasize automation. Demonstrated skill with the following are required:

Have excellent oral, written, verbal and interpersonal communication skills

Preferred Qualifications

Bachelor’s degree and above in a technology related field

Experience with other cloud service providers (e.g. GCP, Azure)

Experience with Kubernetes (CKA, CKAD or CKS)

Experience with AWS AI services such as Bedrock, Bedrock Knowledge Bases, Bedrock AgentCore, Bedrock Prompt Management or similar services

Experience with RAG systems, vector stores, graph databases, semantic search or knowledge management platforms

Experience with MCP, agent orchestration, agent-to-agent workflows or related AI integration patterns

Experience with agent frameworks or orchestration tools such as OpenAI Agents SDK, Google ADK, Strands Agents, LangGraph, CrewAI, LlamaIndex or similar

Experience with monitoring, evaluation or observability tooling for AI-powered systems

Experience with Event-Driven Architecture (Kafka preferred)

Experience using and contributing to Open Source tools

Experience in managing IT compliance and security risk

Demonstrated track record of self-driven learning and a passion for continuously catching up with the rapidly evolving AI ecosystem.

Experience conducting R&D or building proofs-of-concept (PoCs) for emerging AI technologies.

Active engagement with the AI community—evidenced by published papers, technical blogs, open-source contributions, or personal AI hobby projects.

Bilingual in English and Japanese is nice to have, but not required. Proficiency in either language is fine.

Working Conditions

Employment Status

Full Time

Office Location

Hybrid Workstyle (flexible working style including Remote and office) ※ You will be expected to work both in the office and remotely, in alignment with organizational guidelines and team objectives.

LIFE in JAPAN FACTBOOK

Work Hours

Full Flex Time (No Core Time)

In principle, 9:00am ~ 5:45pm (actual working hours: 7h45m + 1h break)

Holidays

Every Sat/Sun/National holidays (In Japan)/New Year's break/Company-designated Special days

Paid leave

Annual leave (up to 14 days in the first year, granted proportionally according to the month of employment. Can be used from the date of hire)

Personal leave (5 days each year, granted proportionally according to the month of employment) ＊PayPay Group's own special paid leave system, which can be used to attend to illnesses, injuries, hospital visits, etc., of the employee, family members, pets, etc.

Salary

Annual salary paid in 12 installments (monthly)

Reviewed once a year

Overtime allowance, Late overtime allowance, Commuting and transportation expenses

Benefits

Social Insurance (health insurance, employee pension, employment insurance and compensation insurance)

401K

Other Information

PayPay Inside-Out (Corporate Blog)

JP

ENG

Recruiting FACTBOOK for PayPay Card

JP