Machine Learning Engineer

Natural Language Processing (NLP)

RAG (Retrieval-Augmented Generation)

core AI initiatives

Responsibilities

• Own the RAG pipeline end to end

  for our customer support initiatives.
• Systematically improve retrieval

  accuracy

  by productionizing techniques like:
• Advanced

  chunking strategies

  for support documents and knowledge bases.

• Embedding model fine-tuning

  on our domain-specific data.

• Hybrid search

  (semantic + keyword) and implementing

  reranker models

  .
• Build and maintain robust

  evaluation frameworks

  to measure system performance and guide improvements.
• Build guardrails with SLMs to control faithfulness, answerability and reduce hallucination rate.
• Ensure Safety and Governance for LLMs used through PII redaction, policy/guardrails integration, grounded citations and incident playbooks.
• Collaborate with backend and data engineers to integrate ML models and search systems into our production environment.
• In the future, design and implement other

  ML models for financial solutions

  , collaborating with data scientists working on different products.

Qualifications

• 6+ years

  of professional experience in machine learning engineering with a proven track record of

  building, deploying, and optimizing end-to-end AI/NLP systems

  in production.
• Educational background in Computer Science, Engineering, Mathematics, or a related field.
• Familiar with

  LLM orchestration

  (LangChain/LlamaIndex or in-house), prompt/version management, tool use, and response streaming.

Core NLP/RAG Skills:

• Deep understanding of

  Natural Language Processing (NLP)

  , including Transformer architectures (e.g., BERT, Sentence-Transformers) and embedding techniques.
• Hands-on experience with

  Vector Databases

  and/or semantic search technologies.
• Expertise in ML frameworks like

  Hugging Face Transformers

  ,

  PyTorch

  , or

  TensorFlow

  .
• Experience with RAG frameworks like

  LangChain

  or

  LlamaIndex

  is strongly preferred.
• Practical experience with evaluation - Golden Sets, A/B testing and RAG specific metrics

General & Financial ML Skills:

• Good understanding of

  supervised and unsupervised learning

  and

  ensemble methods

  .
• Proficiency with libraries like

  scikit-learn

  and

  XGBoost

  .
• Experience in the

  FinTech

  industry or with

  financial/credit data

  is a significant plus.

Core Tech Stack:

• Proficiency in

  Python

  . (Java/Scala is a plus).
• Strong knowledge of database management systems (e.g., MySQL,

  PostgreSQL

  ), ETL processes, and SQL.
• Experience with MLOps practices and tools like

  Docker

  ,

  Kubernetes

  , and cloud platforms (AWS preferred).
• Familiarity with tools like Apache Spark.

Nice to Have

• Multilingual Information Retrieval (especially

  JP/EN

  ), domain ontology/KB design, schema/metadata strategies.
• Safety/guardrails experience, privacy-by-design practices.
• Experience with tooling like FastAPI/gRPC, TorchServe and monitoring with OpenTelemetry/Victoria Metrics.
• Experience with Eval frameworks like

  Ragas, TruLens, DeepEval, LangSmith

  .

Expected Personality

• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Adaptability and a willingness to learn new technologies and techniques.
• Proactive mindset with the ability to think strategically about system improvements.
• Ability to make suggestions and improvements independently.
• Logical communicator with the ability to coordinate smoothly with stakeholders.

Please refer PayPay 5 senses to learn what we value at work.