Job description
Overview
The AI Software Development Engineer – Level 2 (AI SDE2) is responsible for building, integrating, and supporting AI-enabled application features across frontend, backend, and cloud-native environments. This role contributes to the implementation of production-grade software components, AI-powered workflows, and platform integrations, working closely with senior engineers, Technical Leads, and Forward Deployed Engineers.
The AI SDE2 is expected to independently deliver well-defined features, contribute to debugging and system improvement efforts, and support the implementation of modern AI application patterns such as Retrieval-Augmented Generation (RAG), evaluation pipelines, and guardrail mechanisms. The role requires strong engineering fundamentals, fast learning capability, and the ability to operate effectively in fast-paced prototyping and delivery environments.
Responsibilities
Develop and implement application features across frontend and backend systems based on defined technical designs Build and maintain backend services and APIs using Python and FastAPI Develop frontend components and user experiences using React, Next.js/Remix, and Tailwind CSS Implement real-time user interaction capabilities using Server-Sent Events (SSE) or similar streaming mechanisms Contribute to the implementation of AI-powered application features , including LLM integrations and workflow orchestration Support implementation of Retrieval-Augmented Generation (RAG) pipelines, including retrieval logic, ranking improvements, and response optimization Assist in building and maintaining evaluation loops to validate AI model output quality and reliability Implement and maintain guardrails and control mechanisms for safe and reliable AI behavior Participate in debugging, performance tuning, and issue resolution across application and AI workflows Contribute to API integrations and system interoperability across internal and external services Support containerization, deployment, and environment setup using Docker and Kubernetes Contribute to CI/CD pipelines and deployment automation processes Follow security best practices, including authentication, authorization, and secure API integration Maintain observability practices including logging, monitoring, and tracing Document implementation details, blockers, and technical decisions clearly and consistently Requirements Experience 2 to 4 years of software development experience Experience contributing to application development in team-based engineering environments Exposure to AI-enabled applications or data-driven systems preferred Frontend Development Hands-on experience with React Experience building UI components using Tailwind CSS Familiarity with Next.
js or Remix frameworks Understanding of Server-Sent Events (SSE) or similar real-time communication mechanisms Backend Development Strong hands-on experience in Python development Experience building backend APIs using FastAPI Strong understanding of REST API principles , API design, and integration patterns Understanding of backend performance optimization and debugging practices AI / ML Engineering Working knowledge of Large Language Model (LLM) integration concepts Experience or exposure to Retrieval-Augmented Generation (RAG) implementation Understanding of advanced retrieval concepts including Hybrid Search and Re-ranking Familiarity with evaluation loops and model quality validation Understanding of guardrails and AI safety mechanisms DevOps & Platform Engineering Working knowledge of Docker and Kubernetes Understanding of CI/CD pipelines and deployment workflows Familiarity with Infrastructure-as-Code (Terraform) Basic understanding of cloud-native deployment principles Security & Observability Understanding of IAM, OAuth2, and secure authentication flows Familiarity with API security and access control patterns Basic experience with Prometheus, Grafana, and Open.
Telemetry for monitoring and tracing Engineering Practices Strong understanding of software development fundamentals, debugging, and testing practices Ability to follow coding standards, documentation practices, and engineering processes Understanding of version control workflows and collaborative development practices Soft Skills Strong team collaboration and cross-functional communication skills Ability to communicate blockers, risks, and progress clearly Learning agility and willingness to quickly adopt new tools and frameworks Ability to work effectively in fast-paced prototyping and delivery environments Strong ownership mindset for assigned deliverables and system stability Problem-solving mindset with strong analytical thinking and scenario evaluation Discipline in documentation and technical communication Nice to Have Experience with LLM application frameworks such as Lang.
Chain or similar orchestration frameworks Exposure to vector databases and semantic search systems Experience working on AI-first or GenAI product development Exposure to cloud platforms such as AWS, Azure, or GCP Experience working in multicultural or distributed teams Japanese language proficiency preferred for customer-facing or Japan-based roles Language Requirements English: Working proficiency required Japanese: Working proficiency required Develop and implement application features across frontend and backend systems based on defined technical designs Build and maintain backend services and APIs using Python and FastAPI Develop frontend components and user experiences using React, Next.
js/Remix, and Tailwind CSS Implement real-time user interaction capabilities using Server-Sent Events (SSE) or similar streaming mechanisms Contribute to the implementation of AI-powered application features , including LLM integrations and workflow orchestration Support implementation of Retrieval-Augmented Generation (RAG) pipelines, including retrieval logic, ranking improvements, and response optimization Assist in building and maintaining evaluation loops to validate AI model output quality and reliability Implement and maintain guardrails and control mechanisms for safe and reliable AI behavior Participate in debugging, performance tuning, and issue resolution across application and AI workflows Contribute to API integrations and system interoperability across internal and external services Support containerization, deployment, and environment setup using Docker and Kubernetes Contribute to CI/CD pipelines and deployment automation processes Follow security best practices, including authentication, authorization, and secure API integration Maintain observability practices including logging, monitoring, and tracing Document implementation details, blockers, and technical decisions clearly and consistently Experience 2 to 4 years of software development experience Experience contributing to application development in team-based engineering environments Exposure to AI-enabled applications or data-driven systems preferred Frontend Development Hands-on experience with React Experience building UI components using Tailwind CSS Familiarity with Next.
js or Remix frameworks Understanding of Server-Sent Events (SSE) or similar real-time communication mechanisms Backend Development Strong hands-on experience in Python development Experience building backend APIs using FastAPI Strong understanding of REST API principles , API design, and integration patterns Understanding of backend performance optimization and debugging practices AI / ML Engineering Working knowledge of Large Language Model (LLM) integration concepts Experience or exposure to Retrieval-Augmented Generation (RAG) implementation Understanding of advanced retrieval concepts including Hybrid Search and Re-ranking Familiarity with evaluation loops and model quality validation Understanding of guardrails and AI safety mechanisms DevOps & Platform Engineering Working knowledge of Docker and Kubernetes Understanding of CI/CD pipelines and deployment workflows Familiarity with Infrastructure-as-Code (Terraform) Basic understanding of cloud-native deployment principles Security & Observability Understanding of IAM, OAuth2, and secure authentication flows Familiarity with API security and access control patterns Basic experience with Prometheus, Grafana, and Open.
Telemetry for monitoring and tracing Engineering Practices Strong understanding of software development fundamentals, debugging, and testing practices Ability to follow coding standards, documentation practices, and engineering processes Understanding of version control workflows and collaborative development practices Soft Skills Strong team collaboration and cross-functional communication skills Ability to communicate blockers, risks, and progress clearly Learning agility and willingness to quickly adopt new tools and frameworks Ability to work effectively in fast-paced prototyping and delivery environments Strong ownership mindset for assigned deliverables and system stability Problem-solving mindset with strong analytical thinking and scenario evaluation Discipline in documentation and technical communication Nice to Have Experience with LLM application frameworks such as Lang.
Chain or similar orchestration frameworks Exposure to vector databases and semantic search systems Experience working on AI-first or GenAI product development Exposure to cloud platforms such as AWS, Azure, or GCP Experience working in multicultural or distributed teams Japanese language proficiency preferred for customer-facing or Japan-based roles Language Requirements English: Working proficiency required Japanese: Working proficiency required