Job Title
AI Platform Engineer (Data & LLM Infrastructure)
Industry
FinTech / Financial Technology
Job Category
Data Engineering / AI Engineering / Platform Engineering
Career Level
Mid-Senior Level
Location
Tokyo, Japan
Workplace Type
Hybrid
Employment Type
Full-time
Company Overview
A rapidly growing technology company in the financial services sector is transforming how financial products and services are delivered through modern cloud-native platforms, AI technologies, and embedded finance solutions.
The organization partners with major enterprises and financial institutions to modernize legacy systems and develop next-generation digital financial infrastructure. With a strong focus on AI adoption, the company has established a dedicated internal team responsible for scaling AI capabilities across the business and building enterprise-grade AI platforms.
Position Overview
This role sits within a newly established AI-focused engineering team reporting directly to senior technology leadership. The successful candidate will lead the development of enterprise AI infrastructure, data platforms, and LLM-enabled systems designed to support large-scale AI adoption across the organization.
The position combines data engineering, AI platform architecture, cloud infrastructure, and software engineering, offering the opportunity to work with modern technologies and shape AI-driven business transformation initiatives.
Key Responsibilities
Data Platform Engineering
Design, build, and maintain scalable data platforms and data management frameworks.
Develop and optimize ETL pipelines and data integration processes.
Implement solutions for data accessibility, governance, and reliability.
AI Infrastructure & Platform Development
Design and operate secure AI infrastructure environments.
Implement governance mechanisms and sandbox environments under strict security standards.
Build AI gateway services and evaluation frameworks.
LLM & AI Agent Development
Design and implement LLM orchestration frameworks.
Integrate enterprise systems, databases, SaaS platforms, and documentation into AI-enabled workflows.
Build AI agent workflows and evaluation pipelines for business-specific use cases.
Support Retrieval-Augmented Generation (RAG) implementations and continuous optimization.
Software & Cloud Engineering
Design and develop APIs and microservices.
Build cloud-native infrastructure using Infrastructure as Code methodologies.
Collaborate with stakeholders across business and technical teams to deliver AI-driven solutions.
Technology Stack
Python
Go
AWS
Google Cloud
Microsoft Azure
Snowflake
Airflow
dbt
Terraform
OpenAI APIs
Anthropic APIs
Required Qualifications
Data Engineering
Experience building and operating ETL/data pipelines using Python, Go, Java, or similar languages.
Experience working with modern data warehouse platforms such as Snowflake, BigQuery, or Databricks.
LLM & Generative AI
Experience developing applications utilizing LLM APIs such as OpenAI or Anthropic.
Experience implementing and optimizing RAG solutions using frameworks such as LangChain or LlamaIndex.
Software Engineering
Experience designing and developing APIs and microservices (REST, gRPC).
Preferred Qualifications
Experience with Model Context Protocol (MCP) implementations or custom MCP server development.
Experience operating LLM proxy platforms.
Experience working with vector databases such as Pinecone, Weaviate, or pgvector.
Experience building LLM evaluation frameworks using tools such as LangSmith or Ragas.
Experience deploying cloud infrastructure using Terraform or similar IaC solutions.
Experience leading cross-functional initiatives involving multiple stakeholders.
Ideal Candidate Profile
Customer-focused and able to prioritize development work based on business value.
Takes ownership of product and platform growth.
Strong collaboration and stakeholder management skills.
Comfortable working across multiple domains and functions.
Passionate about emerging AI technologies and continuous learning.
Detail-oriented with a strong focus on data quality and operational excellence.
Working Hours
Flexible working arrangements with a hybrid work model.
Working hours may be adjusted according to individual circumstances and business needs. Various working-time systems may apply depending on role level and responsibilities.
Benefits
Comprehensive social insurance coverage
Full transportation allowance
Company-provided laptop
Additional health check subsidies
Employee stock ownership program
Stock option program
Visa sponsorship support
Flexible work arrangements
Family-friendly working policies
No-smoking workplace
Holidays & Leave
Complete two-day weekend (Saturday, Sunday, and public holidays)
Annual paid leave
Consecutive leave program
Year-end/New Year holidays
Bereavement leave
Special leave
Why Join?
Opportunity to build enterprise-scale AI infrastructure from the ground up.
Exposure to cutting-edge technologies including LLMs, AI agents, MCP, cloud-native architecture, and modern data platforms.
High level of autonomy and influence within a strategically important engineering team.
Ability to solve complex challenges involving large-scale financial and alternative data.
Significant impact on organization-wide AI transformation initiatives.