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Langgraph Jobs in Reston, VA (NOW HIRING)

Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen. * Develop and deploy multi-agent systems using Model Context ...

Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen. * Develop and deploy multi-agent systems using Model Context ...

Agentic AI Engineer

Bethesda, MD · On-site

$99K - $225K/yr

Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen. * Develop and deploy multi-agent systems using Model Context ...

Agentic AI frameworks (e.g., LangChain, LangGraph) * RAG (Retrieval-Augmented Generation) architectures * Hugging Face Transformers, PEFT * LLM orchestration frameworks * Experience building scalable ...

Agentic AI Engineer

Washington, DC

$110.40K - $151.10K/yr

Develop multi-agent pipelines using agentic frameworks (e.g., LangChain, LangGraph, or similar). * Implement tool driven workflows for retrieval, reasoning, diagnostics, and decision-making.

Senior AI Developer

Washington, DC

$61.75 - $81.50/hr

Develop multi-agent pipelines using agentic frameworks (e.g., LangChain, LangGraph, or similar). * Implement tool driven workflows for retrieval, reasoning, diagnostics, and decision-making.

Agentic AI Engineer

Washington, DC

$110.40K - $151.10K/yr

Develop multi-agent pipelines using agentic frameworks (e.g., LangChain, LangGraph, or similar). * Implement tool driven workflows for retrieval, reasoning, diagnostics, and decision-making.

Intern, Information Tech

Washington, DC · On-site

$17 - $22.75/hr

Build, test, and deploy applications using frameworks like LangChain, LangGraph, or CrewAI to automate tasks. * RAG Pipelines: Develop and optimize Retrieval-Augmented Generation systems to connect ...

Agentic AI engineer

Washington, DC · On-site

$110.40K - $151.10K/yr

Develop multi-agent pipelines using agentic frameworks (e.g., LangChain, LangGraph, or similar). * Implement tool driven workflows for retrieval, reasoning, diagnostics, and decision-making.

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Langgraph information

What are the key skills and qualifications needed to thrive as a Langgraph engineer, and why are they important?

To thrive as a Langgraph engineer, you need a strong background in software engineering, proficiency in Python, and a solid understanding of AI/ML concepts, usually supported by a degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), API integrations, and version control systems such as Git is essential. Effective problem-solving, collaboration, and clear communication are crucial soft skills for working with multidisciplinary teams and resolving complex issues. These capabilities are important because they enable the development, scaling, and maintenance of robust AI-driven applications using the Langgraph platform.

What are some common challenges faced by Langgraph developers when integrating their workflow with existing AI infrastructure?

Langgraph developers often encounter challenges when integrating their workflow with existing AI infrastructure, such as ensuring compatibility with various large language models and managing data flow across multiple APIs. Coordination with data engineers and machine learning specialists is crucial to align model outputs with business requirements, and adapting to rapidly evolving technologies can require continuous learning. Additionally, optimizing performance and maintaining security standards during integration are key considerations to ensure successful deployment.

What is a Langgraph?

Langgraph is a framework designed to build, manage, and orchestrate complex workflows for large language models (LLMs). It allows developers to create directed graphs of language model prompts, tools, and custom logic, making it easier to design multi-step, stateful AI applications. Langgraph is especially useful for building conversational agents, automated workflows, and other applications that require LLMs to interact with data or tools in a structured way.

What is the difference between Langgraph vs Data Analyst?

AspectLanggraphData Analyst
Required CredentialsTypically requires knowledge of language processing and graph databasesUsually requires a degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI research labs, data-driven organizationsBusiness, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and NLP projectsEstablished role in data interpretation and reporting

While Langgraph focuses on language processing and graph database integration, Data Analysts primarily interpret and visualize data to support business decisions. Both roles require analytical skills, but Langgraph specialists often have a background in AI and NLP, whereas Data Analysts typically hold degrees in statistics or related fields.

What are popular job titles related to Langgraph jobs in Reston, VA? For Langgraph jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Langgraph jobs in Reston, VA look for? The top searched job categories for Langgraph jobs in Reston, VA are:
What cities near Reston, VA are hiring for Langgraph jobs? Cities near Reston, VA with the most Langgraph job openings:
Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP,...

Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP,...

Capital One

Mclean, VA

Full-time

Posted 7 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 134 frontline employees who took The Breakroom Quiz

74th of 141 rated banks


Job description

Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols)

Overview:

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

Team Description:

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business.

The AI Foundations - AI Software Engineering team builds scalable, state-of-the-art AI architectures designed to transform the software development lifecycle at Capital One. Our goal is to empower internal engineers by developing multi-agent solutions that streamline design, code generation, system migration, and troubleshooting to operate software more effectively at scale.To achieve this, we leverage a cutting-edge stack including LangGraph, MCP, Knowledge Graphs, agent-to-agent protocols, and advanced model customization.

In this role, you will:

  • Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.

  • Leverage a broad stack of technologies - Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more - to reveal the insights hidden within huge volumes of numeric and textual data.

  • Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.

  • Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.

  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

The Ideal Candidate:

  • You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.

  • Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

  • Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.

  • A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.

  • Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.

  • Has a deep understanding of the foundations of AI methodologies.

  • Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.

  • An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.

  • Experience in delivering libraries, platform level code or solution level code to existing products.

  • A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.

  • Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Basic Qualifications:

  • Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research

Preferred Qualifications:

  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields

  • LLM

    • PhD focus on NLP or Masters with 5 years of industrial NLP research experience

    • Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)

    • Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)

    • Publications in deep learning theory

    • Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR

  • Agentic AI Research

    • PhD focused on multi-agent systems, autonomous agents, planning, or reinforcement learning

    • Hands-on experience developing and deploying multi-agent architectures (e.g., using frameworks like LangGraph or specialized agent protocols)

    • Experience with techniques like tool-use integration, memory management for agents, or verifiable agent behavior

  • Knowledge Graph Research

    • PhD focused on knowledge representation and reasoning, graph neural networks (GNNs), or large-scale data integration

    • Publications in relevant venues (e.g., ISWC, WWW, KDD, Neurips, ICML) on knowledge graph construction, embedding, querying, or reasoning

    • Experience in designing, implementing, and deploying industrial-scale Knowledge Graph solutions

    • Demonstrated expertise with graph databases (e.g., Neo4j, JanusGraph) and graph embedding techniques

  • Finetuning

    • PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)

    • Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance

    • Experience deploying a fine-tuned large language model

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Cambridge, MA: $218,700 - $249,600 for Applied Researcher I


McLean, VA: $218,700 - $249,600 for Applied Researcher I


New York, NY: $238,600 - $272,300 for Applied Researcher I


San Francisco, CA: $238,600 - $272,300 for Applied Researcher I


San Jose, CA: $238,600 - $272,300 for Applied Researcher I







Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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