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About the Role

AI Team is responsible for developing and operating AI-powered products such as moderation for Pairs' Trust & Safety, and recommendations. This MLOps Engineer will be embedded within AI product teams, balancing product reliability with development speed. The role involves end-to-end ownership of Kubernetes clusters, common components, online inference APIs, asynchronous workers, batch processing, and continuous learning pipelines. Collaboration with SRE, Security, Privacy, and Product teams is key to building the AI product foundation. The primary mission is to ensure AI products run stably in production and can be improved quickly and safely, rather than focusing on model research and development itself. Expectation to reduce operational toil through automation and to foster an environment where humans and AI agents can develop together safely and quickly by contributing knowledge gained from work and implementation into harnesses for AI agents.

Responsibilities

Manage Kubernetes (Amazon EKS) cluster version upgrades, node group, and autoscaling configurations.
Maintain and improve cluster components managed by GitOps (Argo CD / Helm / Kustomize).
Redesign observability, monitoring, and alerting for AI applications, including metrics, logs, traces, actionable alerts, and Runbooks.
Automate initial alert investigations using AI agents.
Collaborate with SRE teams to define and operate SLI/SLO for AI applications, including setting targets based on use case impact, implementing burn rate alerts, and establishing triage flows between teams.
Design and operate systems for continuous updates of dependency libraries, Python runtimes, base images, and security patches for numerous Python applications within AI products.
Lead migrations associated with LLM model and SDK updates (e.g., generational transitions for Claude on Amazon Bedrock, GPT models, and Gemini models on Gemini Enterprise Agent Platform), ensuring quality regression checks and phased rollouts.
Promote security and data protection for the AI foundation in collaboration with Security and Privacy teams, addressing vulnerabilities and managing permissions related to supply chain attacks and AI-enhanced attacks, and handling training data and logs according to data retention policies.
Operate and improve continuous learning pipelines, training environments, and dataset preparation environments using Argo Workflows and Amazon SageMaker.
Operate and improve environments on the Gemini Enterprise Agent Platform (formerly Vertex AI) used for recommendation model training and batch processing.
Operate online inference APIs, queue-driven asynchronous workers, and batch applications running on Kubernetes, focusing on capacity planning, cost optimization, incident response, and CI/CD/release flow improvements.
Reduce daily development and operational toil by leveraging coding agents and AI agents.
Establish a system for continuously improving harnesses (documentation, linting, testing, CI checks, etc.) that AI agents can reference and verify, based on knowledge gained from daily work and implementation.

Requirements

3+ years of practical experience in implementing, releasing, and operating commercial applications.
Production application development experience, including package management, type checking, and testing.
Experience creating and operating container images for production environments (e.g., Dockerfile creation, middleware installation).
Production operational experience with applications on Kubernetes.
Deep understanding of either AWS or Google Cloud, with experience building and operating production infrastructure.
Experience in incident response and system improvement using metrics, logs, and traces.
Experience designing and operating batch and asynchronous processing that considers idempotency and retries.
Experience developing with coding agents and a strong desire to utilize AI.
Ability to understand how one's own code executes in the runtime environment and to proactively resolve issues.
Ability to prioritize based on value to the product and business, rather than making infrastructure creation an end in itself.
Ability to balance technical ideals with reality and drive progress through consensus with stakeholders.
Language proficiency: Japanese (Business level or higher), English (Conversational level or higher).

Skills

Python
Go
Shell
FastAPI
Uvicorn
LightGBM
scikit-learn
pandas
Polars
PyTorch
Amazon Bedrock (Claude, GPT)
Google Gen AI SDK (Gemini)
Amazon EKS
Argo CD
Argo Workflows
KEDA
External Secrets Operator
OPA Gatekeeper
Cluster Autoscaler
Helm
Kustomize
Amazon SageMaker
Amazon SQS
Amazon S3
Amazon Athena
AWS Glue
Amazon Aurora (MySQL compatible)
Amazon DynamoDB
BigQuery
Gemini Enterprise Agent Platform (formerly Vertex AI)
Terraform
Datadog (APM, Logs, Monitors, Synthetics)
Fluent Bit
Wiz
Git
GitHub
GitHub Actions
Docker
uv
Ruff
mypy
pytest
Renovate / Dependabot
Jira Cloud
Confluence
Slack
Claude Code Enterprise
Codex / ChatGPT Enterprise
Cursor Enterprise
Gemini Enterprise

Location

Japan

Work Type

Full-time

Experience Level

3+ years of practical experience

About the Company

We are responsible for the planning and operation of the dating and matchmaking app 'Pairs', aimed at those seriously seeking a partner. Service started in Japan in 2012, followed by releases in Taiwan in 2013 and South Korea in 2025. Cumulative registrations exceed 27 million (as of April 2026), with a large user base. Over 900,000 individuals have found irreplaceable partners through Pairs.