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I will build a scalable, secure rag pipeline for financial document QA on AWS
Pree K
About this gig
AWS Certified ML Specialty | FinTech AI Systems
Build a production-ready Retrieval Augmented Generation (RAG) pipeline for accurate, source-cited Q&A over your internal documents on AWS.
This service is designed for financial and regulated environments where accuracy, traceability, and data privacy are critical. I dont build generic chatbots I build reliable AI systems that work with policy documents, compliance manuals, advisor notes, and internal knowledge bases.
What you get
- End to end RAG pipeline on AWS
- Document ingestion and chunking strategy
- Embeddings and vector search setup
- Retrieval tuning for high quality answers
- Source cited responses
- Structured outputs for downstream systems
- Optional evaluation and guardrails
Why work with me
- Senior Data Scientist in FinTech
- Built production LLM systems for financial coaching and compliance
- Experience with PII redaction, validation layers and human in the loop workflows
- AWS Certified AI Practitioner
- AWS Certified Machine Learning Specialty
Please message before ordering so I can tailor the solution to your needs.
Get to know Pree K
Pree K
Senior Data Scientist
- FromUnited Kingdom
- Member sinceMar 2026
Languages
English, Hindi, French
I am a Senior Data Scientist at Octopus Money. I design and implement customer persona clustering models and build scalable ML pipelines using AWS SageMaker. I also develop agentic AI workflows and LLM systems for financial coaching.
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FAQ
What is a RAG pipeline?
A RAG pipeline retrieves relevant information from your documents and uses AI to generate accurate, source-cited answers instead of guessing.
How is this different from a chatbot?
This is not a generic chatbot. It provides traceable, source-backed answers with higher accuracy and reliability, especially for financial and compliance use cases.
Can you work with sensitive financial data?
Yes. I design systems with privacy in mind and can include PII detection and redaction, along with secure AWS-based setups.
Will the system provide citations or sources?
Yes. The system is designed to return answers with references to the original documents, improving trust and auditability.
Do you deploy on my AWS account?
Yes. I can build and deploy the solution within your AWS environment using services like Lambda, S3, and Bedrock.
Can this integrate with my existing app?
Yes. I can expose the system via API so it can integrate with your application, chatbot, or internal tools.
What do you need from me to get started?
Typically: Sample documents or data sources Use case (internal search, advisor tool, compliance, etc.) Preferred AWS setup (if available)
