Senior Lead Software Engineer, Full Stack, AI Platform & Knowledge Library

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: New York, NYMcLean, VARichmond, VA
Employment: Full-time
Posted: 85 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $183k

$107k $243k

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This listing doesn't post a salary. Most similar roles pay $151,000–$214,750.

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About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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At a glance

TL;DR · Senior Lead Software Engineer, Full Stack, AI Platform & Knowledge Library

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The Senior Lead Software Engineer, Full Stack (AI Platform & Knowledge Library) (Enterprise Platforms Technology) joins the Enterprise Platforms Technology team to own the AI platform and knowledge infrastructure for marketing content generation. You will design and build a Content Moderation Knowledge Library, own the embedding search pipeline, and lead Prompt MLOps to improve generation quality. Key responsibilities include owning AI content compliance and generation platform APIs, designing agentic critic validation streams, and mentoring engineers on RAG system design. The role requires proficiency in Python, cloud computing including AWS services like S3, Lambda, and SQS, and vector search infrastructure such as OpenSearch, Pinecone, PGvector, or Weaviate. You will solve the technical problem of creating a shared knowledge platform to ensure compliant, on-brand content generation at scale.

Skills

Python RAG LLM AWS OpenSearch Pinecone PGvector Weaviate MLOps S3 Lambda SQS Microsoft Azure Google Cloud Vector Search

What you'll do

Build a RAG-backed Content Moderation Knowledge Library covering regulatory requirements and brand rules.
Implement and tune retrieval pipelines including chunking strategies, embedding model selection, and hybrid search.
Lead Prompt MLOps by treating prompts as versioned artifacts and managing the continuous evaluation loop.
Own and manage the AI content compliance and generation platform APIs as shared capabilities.
Design an agentic critic validation stream using deterministic validators and LLM-as-judge semantic validators.
Lead technical designs, produce architecture documents, and conduct design reviews for RAG systems.
Mentor engineers on RAG system design, evaluation methodology, and production AI operations.

What we're looking for

At least 6 years of experience in software engineering.
At least 1 year of experience with cloud computing (AWS, Microsoft Azure, Google Cloud).
Master’s Degree (preferred).
4+ years of experience in Python (preferred).
4+ years of experience building and operating retrieval-augmented generation (RAG) systems (preferred).
3+ years of experience in applied AI/ML or LLM-based systems in production (preferred).
3+ years of experience with vector search infrastructure (OpenSearch, Pinecone, PGvector, Weaviate, or equivalent) (preferred).

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