Full Stack Engineer, GenAI & Agentic Systems

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: McLean, VARichmond, VAChicago, ILPlano, TXSan Francisco, CASan Jose, CANew York, NY
Salary: $179,400–$204,700 / yr
Employment: Full-time
Posted: 4 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $208k

This role $192k

$174k most similar roles pay here $231k

This role pays less than 65% of similar roles. Most pay $192,050–$223,750 — the shaded band above. At the midpoint, this role pays about $192k versus about $208k for comparable roles.

Based on 240 similar postings.

Employer

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 936 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 933 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Full Stack Engineer, GenAI & Agentic Systems

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Full Stack Engineer 4- People Tech- GenAI & Agentic Systems (LangChain, LangGraph) joins the People Tech team to drive digital transformation for corporate functions like HR and Legal. You will architect and deploy autonomous agentic AI solutions, multi-agent workflows, and LLM-driven capabilities to automate complex processes and enhance the global associate experience. The role involves leading a portfolio of projects, mentoring engineers, and collaborating with product managers to build secure, cloud-based systems. You will work with technologies including Java, Scala, Python, Go, Node.js, JavaScript, and TypeScript, alongside RDBMS and NoSQL databases. Technical requirements include experience with Docker, Kubernetes, AWS tools, LangChain, LangGraph, and RAG architecture. You will solve business problems by building scalable platforms that integrate AI-enabled features into the enterprise infrastructure while ensuring compliance and reducing technical debt through proactive refactoring.

What does a Full Stack Engineer earn in Virginia?

Median $211200 from 149 postings across 4 companies.

See salary data

Skills

LangChain LangGraph LLM Python Java Go Node.js TypeScript JavaScript AWS GCP Azure Docker Kubernetes SQL NoSQL RAG CI/CD Infrastructure as Code Microservices

What you'll do

Architect LLM-driven capabilities and HRTech innovations to optimize enterprise-scale HR processes.
Design, build, and deploy autonomous agentic AI solutions and multi-agent workflows for global operations.
Ensure all AI-enabled capabilities are implemented in a secure, compliant, and well-managed manner with risk teams.
Lead a portfolio of distributed microservices and full-stack systems to meet regulatory requirements.
Provide technical leadership and set standards for design, development, and operational excellence across Agile teams.
Partner with architects to define service boundaries, integration patterns, and reusable AI execution frameworks.
Identify areas for improvement by performing targeted refactoring to reduce technical debt and enhance performance.
Influence long-term technology strategy by evaluating emerging tools and practices in a "you build it, you own it" environment.

What we're looking for

Bachelor's Degree or higher in Computer Science or a related quantitative field.
At least 4 years of experience in software engineering.
At least 2 years of experience with public cloud platforms, cloud-based productivity tools, or virtualization technologies.
Master’s Degree in Computer Science or a related field (preferred).
7+ years of experience in coding with more than two of Python, Java, JavaScript, TypeScript, SQL, or Go (preferred).
3+ years of experience with database and database management systems (preferred).
4+ years of experience contributing to open source frameworks (preferred).
Experience with RAG architecture, vector databases, or agentic LLM pipeline design using LangGraph or LangChain (preferred).

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