You will oversee Autify’s backend development and build the infrastructure, the AI-based software test automation platform and the data pipeline. In order of importance, this person will support infrastructure and platform development, data engineering, and machine learning.
Our team
Our engineering team is composed of 14 members, of developers, QA engineers, leadership, SRE.We are all from different backgrounds and work efficiently in a multi-cultural environment. The team is fully agile, and AI enabled, with all teams building their own agentic flows, both in product and in the pipeline.
Our development process
Given the nimble nature of small teams and speed of development required in 2026 the teams are focusing on fine tuned processes, and tooling, adapted to their own needs.
Many traditional roles and responsibilities remain, with our product team prioritizing and defining the roadmap with our stakeholders, QA engineers designing the testing scope and framework through our own products. For the SRE team in particular, the process is project oriented, with a kanban based ticketing approach for operational requests, and a more focused time for long tailed deliveries.
Tech stack
- Backend: Node, Go, Ruby, Ruby on Rails
- Frontend: JavaScript, TypeScript, React, Redux, Styled Components
- Machine learning: Python
- Database: PostgreSQL, Redis
- Test: Autify Nocode, Autify Nexus, Autify Aximo
- Infrastructure: AWS, Docker, GCP, Terraform, Packer
- Monitoring: Sentry, Datadog, Drata
- CI/CD: CircleCI, GitHub Actions
- Source code management: GitHub
- Project management: Internal
- Misc.: G Suite, Slack, Notion, Discord, 1Password, etc.
Responsibilities
- Infrastructure development using AWS and GCP
- Manage containers, Terraform, etc
- Build and operate clean, reliable data pipelines
- Keep our systems up and running to meet our SLO
- Develop and maintain AI-powered systems to improve and operate our systems
- Develop and maintain the MLOps platform and CI/CD pipelines for training, deploying, and serving machine learning models.
- Collaborate with the peer teams to operationalize and scale machine learning systems.
- Support the engineering teams in research, innovation, optimization and validation of different capabilities for our ML based offering
Requirements
- Over 3 years of experience in developing, deploying, and operating highly reliable backend systems and infrastructure
- Over 1 year of experience in building infrastructure using container management services such as Docker, Kubernetes, and AWS Elastic Container Service
- Over 1 year of experience in data engineering, specifically including:
- Developing and maintaining data pipelines
- Managing data governance aspects such as data classification, retention, and secure data access, particularly in development and test environments
- Experience in technology selection and and system architecture design
- Experience in improving SLA/SLO
- English skills (Business level or better)
- The right to work in Japan or the ability to qualify for a work visa. Preference is given to those already living in Japan.
Nice to haves
While not specifically required, tell us if you have any of the following.
- Experience with data pipelines and database management
- Experience with MLOps:
- developing and maintaining ML CI/CD pipelines for model lifecycle
- implementing observability for production ML model performance and data quality
- Modern development experience with Agile methodologies.
- Experience in an on-call rotation
Compensation
¥8,500,000 ~ ¥10,000,000 annually.
