Job Description

Locations

• CA, San Francisco
• VA, McLean
• MA, Cambridge
• CA, San Jose
• NY, New York

Salary

Skills Required

• LLMOpsintermediate

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or AI plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 6 years of experience developing AI and ML algorithms or technologies (experience, 8 years)
• At least 8 years of experience programming with Python, Go, Scala, or Java (experience, 8 years)
• Bachelor's degree in Computer Science, Engineering, or AI plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 6 years of experience developing AI and ML algorithms or technologies (experience, 8 years)
• At least 8 years of experience programming with Python, Go, Scala, or Java (experience, 8 years)

Preferred Qualifications

• 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) (experience, 8 years)
• 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) (experience, 2 years)
• 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) (experience, 2 years)
• 8+ years of experience designing mission-critical machine learning platforms (experience, 8 years)
• 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems (experience, 2 years)
• Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level (experience)
• Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang (experience)
• Master's degree in Computer Science, Computer Engineering, or relevant technical field (degree in computer science)
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (experience)
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers (experience)
• Experience leading GenAI or LLM-Powered application architectures in production (experience)
• Deep understanding of Responsible AI, data privacy and multi-tenant security patterns (experience)
• Experience as a Staff-plus or Distinguished IC engineer influencing 50+ engineers and C-suite stakeholders (experience)
• K8s mastery (multi-region clusters, sericie mesh) (degree)
• Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production (experience)
• 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) (experience, 8 years)
• 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) (experience, 2 years)
• 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) (experience, 2 years)
• 8+ years of experience designing mission-critical machine learning platforms (experience, 8 years)
• 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems (experience, 2 years)
• Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level (experience)
• Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang (experience)
• Master's degree in Computer Science, Computer Engineering, or relevant technical field (degree in computer science)
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (experience)
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers (experience)
• Experience leading GenAI or LLM-Powered application architectures in production (experience)
• Deep understanding of Responsible AI, data privacy and multi-tenant security patterns (experience)
• Experience as a Staff-plus or Distinguished IC engineer influencing 50+ engineers and C-suite stakeholders (experience)
• K8s mastery (multi-region clusters, sericie mesh) (degree)
• Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production (experience)
• Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. (experience)
• 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. (experience)
• 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. (experience)
• Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. (experience)
• For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com (experience)
• 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. (experience)
• 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). (experience)

Responsibilities

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement.
• A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them.
• Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%.
• Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents.
• You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG.
• You’ll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs
• Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities.
• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement.
• A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them.
• Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%.
• Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents.
• You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG.
• You’ll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs
• Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities.

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