Description

Key Responsibilities

AI Developer Tooling

 Build and maintain FTI's internal AI Developer Platform:

 Codebase-aware AI coding assistant (configured with NRI project and domain

context)

 AI-generated test case system: generate test cases from requirements

documents with NRI quality standard alignment

 Intelligent code review assistant: flag issues against NRI coding standards

automatically

 Develop and maintain the Japanese-to-English specification translation

engine: LLM-based translation of Japanese IT requirements and design

documents into structured English work packages for FTI engineering teams,

incorporating NRI-specific terminology

 Instrument and maintain productivity metrics dashboards: measure AI tool

impact on sprint velocity, defect density, code coverage, and time-to-delivery

GenAI Client Solutions

 Design and implement GenAI/LLM-based solutions for NRI Group clients:

 RAG systems for document intelligence (requirements, contracts, regulatory

documents)

 LLM-powered business process automation and AI agents for structured

workflows

 Domain-specific LLM applications for financial services (market data,

compliance, operations)

 Manage the full GenAI solution lifecycle: data ingestion, embedding pipeline,

retrieval, reranking, evaluation, and production monitoring

Legacy Modernisation AI Tooling

 Contribute to FTI's Legacy Modernisation CoE toolchain:

 Automated analysis and documentation of legacy codebases (COBOL, PL/I,

old Java, RPG)

 AI-generated dependency maps and refactoring recommendations

 Code translation and equivalent generation in modern stacks (Java, Python,

TypeScript)

 Migration risk assessment and effort estimation tools

Team Collaboration

 Work with FTI's SWE, QA, and Infra teams to embed AI tools into their

delivery workflows

 Mentor junior AI engineers and contribute to team technical standards

Required Qualifications

 Experience: 4+ years in software development; 2+ years in AI/ML/GenAI

engineering with production deployment experience

 Programming: Strong Python (primary); proficiency in at least one of Java,

TypeScript, Go

 LLM/GenAI: Hands-on experience with LLM APIs (OpenAI, Anthropic Claude,

or open-source models); GenAI frameworks (LangChain, LlamaIndex, or

equivalent)

 RAG: End-to-end RAG pipeline implementation: document processing,

chunking, embedding models, vector database management, retrieval

optimisation, reranking

 Vector databases: Practical experience with at least one of Pinecone,

Weaviate, Chroma, Milvus, or pgvector

 Cloud: Working knowledge of AWS, Azure, or GCP including managed AI/ML

services

 Software engineering fundamentals: Git, CI/CD, testing practices, code

review culture

Preferred Qualifications

 Experience building internal developer platforms or developer productivity

tools

 Experience with legacy code analysis tools, AST parsing, or automated

refactoring

 Financial services domain knowledge (banking, securities, asset

management)

 Experience with multi-lingual NLP, machine translation, or document

processing pipelines

 Familiarity with prompt engineering, few-shot learning, and LLM evaluation

methodologies

Key Skills

Education

Bachelor's degree