Currently tracking 241 active AI roles, down 26% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $123k–$392k (avg $231k).
Capital One currently has 293 active AI-related job listings. The majority of these roles are focused on serving infrastructure, accounting for 28% of the total, followed closely by agents at 26% and post-training at 23%. Engineering is the dominant function, with 234 roles, and hiring is primarily concentrated in the United States. Frequent tech tags include model_serving, vector_db, and llm_observability, suggesting a focus on the operational aspects of AI deployment. In the last 30 days, Capital One posted 124 new AI roles, representing a 22% increase compared to the previous 30-day period.
Capital One currently has 305 active AI-related roles in our index. The most common open titles are: Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (9), Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (8), Applied Researcher I (6), Distinguished Engineer (6), Applied Researcher II (5). Most positions are in Engineering and Research.
Capital One's active AI hiring is concentrated in: serving infrastructure (28%), agents (27%), post-training (23%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Capital One is hiring AI talent in: United States (299 roles), United Kingdom (3 roles), Canada (2 roles), Philippines (1 role).
Job postings at Capital One most frequently reference: model serving, vector db, fine tuning, llm observability, inference infra.
In the past 30 days, Capital One has posted 96 new AI-related roles. That is a -26% change versus the prior 30 days (130 → 96).
| Title | Stage | AI score |
|---|---|---|
| Senior Lead AI Engineer (GenAI Platform Services) Senior Lead AI Engineer responsible for designing, developing, testing, deploying, and supporting AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves optimizing LLM performance for scalability, cost, and latency, and contributing to the technical vision and roadmap of foundational AI systems. | ServeAgent | 8 |
| Senior Lead AI Engineer (FM Hosting) This role focuses on designing, developing, testing, deploying, and supporting AI software components, including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large-scale production AI systems and contribute to the technical vision and roadmap of foundational AI systems. The role involves leveraging various AI technologies and optimizing for scalability, cost, latency, and throughput. |
| ServeAgent |
| 8 |
| Distinguished Engineer Distinguished Engineer role focused on building and scaling agentic AI systems for marketing and sales within an enterprise context. Responsibilities include prototyping, defining technical strategy, influencing architecture, and mentoring teams, with a focus on real-time, hyper-personalized customer engagement and assistive technologies for sales teams. | Agent | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on LLM inference and optimization for AI-powered products within a large enterprise. The role involves designing, developing, and deploying AI software components, with a strong emphasis on improving the performance, scalability, cost, and latency of production AI systems. | ServeAgent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, and observability. It also requires inventing and introducing LLM optimization techniques to improve performance (scalability, cost, latency, throughput) of large-scale production AI systems. The role leverages AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch. | ServeAgent | 8 |
| Sr. Lead AI Engineer This role focuses on designing, developing, testing, deploying, and supporting AI software components, including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will also invent and introduce LLM optimization techniques to improve the performance (scalability, cost, latency, throughput) of large-scale production AI systems, leveraging technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails. The role is within the Intelligent Foundations and Experiences (IFX) team, aiming to advance AI science and engineering and deploy proprietary solutions. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and deploying AI-powered products, including foundation model training, LLM inference, similarity search, guardrails, and model evaluation. The role involves leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails, and optimizing LLM performance for scalability, cost, and latency. The position requires a strong engineering foundation and experience in AI/ML algorithm development and deployment on cloud platforms. | AgentServe | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer role focused on AI Foundations, LLM Core, and Agentic AI. Responsibilities include designing, developing, testing, deploying, and supporting AI software components like foundation model training, LLM inference, similarity search, guardrails, model evaluation, and governance. The role involves optimizing LLM performance for scalability, cost, and latency, and contributing to the technical vision for foundational AI systems. It requires experience with cloud platforms and AI/ML algorithms, particularly LLM inference, similarity search, vector databases, and guardrails. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. Key responsibilities include optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and techniques. | ServeAgent | 8 |
| Senior Lead AI Engineer (LLM Customization and Finetuning) Senior Lead AI Engineer focused on LLM customization and finetuning within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It requires leveraging AI technologies like Huggingface, VectorDBs, and PyTorch, and optimizing LLM performance for scalability, cost, latency, and throughput in production AI systems. The candidate should have a strong engineering and mathematics foundation, experience with cloud platforms, and the ability to lead and mentor teams. | Post-trainServe | 8 |
| Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols) Applied Researcher focused on building multi-agent AI systems, leveraging knowledge graphs, GraphRAG, Graph-of-Thought, LangGraph, and agent protocols to transform the software development lifecycle. The role involves applied research, model development, and delivering AI-powered products. | Agent | 8 |
| Senior Lead AI Engineer (FM Hosting, LLM Inference) Senior Lead AI Engineer focused on optimizing LLM inference performance, scalability, cost, and latency for production AI systems within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, and deploying AI software components, including foundation model training, inference, similarity search, guardrails, evaluation, and observability, leveraging cloud platforms and open-source AI technologies. | ServeAgent | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on optimizing LLM inference for scalability, cost, and latency within an enterprise AI setting. The role involves designing, developing, and deploying AI software components, including foundation model training, inference services, similarity search, guardrails, and model evaluation, leveraging cloud platforms and various AI technologies. | ServeAgent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer role focused on AI Foundations, LLM Core, and Agentic AI. Responsibilities include designing, developing, testing, deploying, and supporting AI software components such as foundation model training, LLM inference, similarity search, guardrails, model evaluation, and agentic systems. The role involves optimizing LLM performance for scalability, cost, and latency, and contributing to the technical vision and roadmap for foundational AI systems. Requires strong engineering and mathematics foundation, expertise in Python/Go/Scala/Java, and experience with cloud platforms and AI technologies like Huggingface, VectorDBs, and PyTorch. | AgentServe | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and deploying AI-powered products, including foundation model training, LLM inference, similarity search, guardrails, and model evaluation. The role involves leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails, and optimizing LLM performance for scalability, cost, and latency. The position requires a strong engineering foundation and experience in AI/ML algorithm development and deployment on cloud platforms. | AgentServe | 8 |
| Director, AI Engineering Director of AI Engineering responsible for the strategic vision, development, and management of autonomous AI systems and platforms, with a focus on agentic workflows and ensuring compliance with regulations and ethical AI best practices within a fintech domain. | Agent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also focuses on optimizing LLM performance (scalability, cost, latency, throughput) for production AI systems and contributing to the technical vision and roadmap of foundational AI systems. | AgentServe | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also focuses on optimizing LLM performance (scalability, cost, latency, throughput) for production AI systems and contributing to the technical vision and roadmap of foundational AI systems. | AgentServe | 8 |
| Lead AI Engineer Lead AI Engineer role focused on designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves leveraging AI technologies and optimizing LLM performance for scalability, cost, latency, and throughput within an enterprise AI context. The position emphasizes building AI-powered products and foundational AI systems for millions of customers. | ServeAgent | 8 |
| Senior Manager, Data Scientist - US Card (Generative AI Systems) Senior Manager, Data Scientist for the Generative AI Systems (Genesis) team within Capital One's US Card division. This role focuses on building and delivering state-of-the-art generative AI-based solutions for various applications including dialogue, text summarization, reading comprehension, speech recognition, image/document processing, and time-series modeling. The goal is to enhance both internal business efficiency and customer-facing experiences. The role involves partnering with cross-functional teams, leveraging a broad tech stack, and building ML models through all phases of development. | ShipPost-train | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also includes optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and cloud platforms. The role emphasizes building responsible and reliable AI systems for banking applications. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also includes optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and techniques. | ServeAgent | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability. It also emphasizes inventing and introducing LLM optimization techniques to improve the performance of large-scale production AI systems. | ServeAgent | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and deploying AI-powered products, including foundation model training, LLM inference, similarity search, guardrails, and evaluation. The role involves optimizing performance, scalability, cost, and latency of large-scale production AI systems using various AI technologies and cloud platforms. | ServeAgent | 8 |
| Manager, Data Science - AI Foundations Manager, Data Science - AI Foundations role at Capital One focuses on building and shipping AI/ML solutions for customer-facing applications, including personalization and digital assistants. The role involves leveraging LLMs, fine-tuning them, and building ML/NLP models through all development phases, from design to production operationalization. It emphasizes partnering with cross-functional teams and utilizing technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. | Post-trainServe | 8 |
| Principal Data Scientist, AI Foundations This role focuses on building and shipping AI/ML solutions for Capital One's mobile app, leveraging LLMs and generative AI. The Principal Data Scientist will partner with cross-functional teams to deliver AI-powered products, adapt and fine-tune LLMs for customer-facing applications, and build ML/NLP models through all phases of development, including training, evaluation, and validation, with a strong emphasis on operationalizing them in production systems serving millions of customers. Experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF are required, along with a track record of delivering models at scale. | Post-trainServe | 8 |
| Senior Manager, Data Science - AI Foundations Senior Manager, Data Science - AI Foundations role at Capital One, focusing on building and shipping AI/ML solutions for their mobile app. The role involves partnering with cross-functional teams, leveraging technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs, and specializing in NLP and LLMs for customer-facing applications. Responsibilities include model development from design to production, operationalization, and translating complex work into business goals. The ideal candidate is customer-first, innovative, creative, a leader, technical with hands-on LLM experience, and influential. Experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF is required, along with a track record of delivering models at scale in both training and inference. | Post-trainServe | 8 |
| Manager, Data Scientist - Business Cards & Payments Credit Infrastructure Team Capital One is seeking a Manager, Data Scientist for their Business Cards & Payments Credit Infrastructure Team. This role involves building machine learning models for credit card acquisitions valuations, supporting credit programs through robust models and tools. The candidate will partner with cross-functional teams, leverage technologies like Python, AWS, and Spark, and translate complex work into business goals. The ideal candidate is customer-focused, creative, a leader, technically proficient with open-source tools and cloud platforms, and statistically minded with experience in various modeling techniques. | Post-train | 7 |
| Principal, Data Scientist - Card Intelligence This role focuses on building and deploying machine learning models for the credit card lifecycle, including marketing, underwriting, and fraud prevention. The candidate will work with a cross-functional team, leverage technologies like Python and AWS, and be responsible for all phases of model development from design to implementation. The role requires a strong statistical background and experience with various machine learning techniques. | Post-train | 7 |
| Senior Associate, Data Scientist - Card Intelligence Data Scientist role focused on building and deploying machine learning models for the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. The role involves leveraging customer data, applying various ML techniques, and working with technologies like Python, AWS, and Spark. | Post-train | 7 |
| Senior Lead Software Engineer, Full Stack (Enterprise Platform Technology) This role focuses on building and operating AI-native software engineering harnesses, including agentic workflow orchestration systems, to enable governed AI model and coding agent delivery. It involves defining contracts, developing observability for AI-assisted workflows, integrating evaluation results into CI/CD, and building reusable APIs and services for AI-native engineering practices within an enterprise platform. | AgentEval Gate | 7 |
| Principal Associate, Data Scientist - People Strategy & Analytics Data Scientist role focused on applying AI/ML, specifically LLMs with RAG and prompt engineering, to HR talent decisions. The role involves building NLP and ML models through all development phases, partnering with cross-functional teams, and leveraging technologies like Python, SQL, AWS, LangChain, Hugging Face, VectorDBs, and Pytorch/TensorFlow. The ideal candidate is innovative, creative, technical, and statistically-minded. | AgentPost-train | 7 |
| Senior Associate, Data Scientist - People Strategy & Analytics Senior Associate, Data Scientist role focused on People Strategy & Analytics, applying AI/ML to talent decisions. Responsibilities include developing NLP and ML models, using LLMs with RAG and evaluation frameworks, partnering with cross-functional teams, and leveraging technologies like Python, SQL, AWS, LangChain, Hugging Face, and VectorDBs. The ideal candidate is innovative, creative, technical, and statistically-minded. | AgentPost-train | 7 |
| Senior Manager, Data Science, Bank Operations Senior Manager, Data Science for Bank Operations at Capital One. This role involves building machine learning models for core internal capabilities like Check/Document Reading, Anomaly Identification, NLP of Calls, and operational forecasting, utilizing technologies such as neural networks, LLMs, and transformer architectures. The candidate will partner with cross-functional teams, leverage technologies like Python, AWS, H2O, and Spark, and manage the full lifecycle of ML model development. | Post-train | 7 |
| Principal Associate, Data Scientist - Bank Operations Data Science The Bank Operations Data Science team builds machine learning models for internal capabilities like Check/Document Reading, Anomaly Identification, Natural Language Processing of Calls, and operational forecasting, using technologies like neural networks, LLMs, and transformer architectures. The role involves partnering with cross-functional teams, leveraging technologies like Python and Snowflake, and building ML models through all phases of development. The ideal candidate is innovative, creative, technical, and statistically-minded, with experience in various ML techniques and cloud platforms. Specific experience with LLM risk mitigation is preferred. | Post-train | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer role focused on productionizing ML applications and systems at scale within a fintech company. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing code, automating tests and deployment, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring responsible AI practices. The role emphasizes scaling ML solutions and integrating them into production environments. | ShipServe | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer role focused on productionizing ML applications and systems at scale within a fintech company. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing code, automating tests and deployment, collaborating in Agile teams, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code quality, risk governance, and Responsible AI. Requires experience in data-intensive solutions, programming, scaling ML systems, and leading ML development teams. | ServeData | 7 |
| Data Analyst Manager - Model Risk Office Manager for a Data Analyst team focused on automating model risk management processes using AI and ML, including building conversational chatbots and multi-agent systems. The role involves optimizing data integration and driving strategy through analytics, with a strong emphasis on AI-powered automation. | AgentServe | 7 |
| Manager, Data Scientist - Partnerships Acquisitions This role is for a Manager, Data Scientist focused on Partnerships Acquisitions within Capital One's fintech domain. The primary responsibility is to lead the development of next-generation machine learning models for credit decisioning, including application approval/decline, product optimization, and customer valuation. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building models through all phases of development. The ideal candidate has a strong technical and statistical background with hands-on experience in data science solutions and machine learning. | Post-train | 7 |
| Director, Data Science Director of Data Science at Capital One Canada, responsible for managing the risk and uncertainty in statistical models, leading the architecture and development of ML models through all phases, and leveraging technologies like Python and AWS, including agentic AI. The role involves managing talent and investigating new technologies for digital banking. | ServePost-train | 7 |
| Principal Associate, Data Scientist - Partnerships Acquisitions Capital One is seeking a Principal Associate Data Scientist to join their Partnerships Acquisitions team. This role focuses on building and implementing machine learning models for credit decisioning, product optimization, and customer valuation within the fintech domain. The position requires a strong background in data science, machine learning, and statistical modeling, with experience in Python, AWS, and SQL. The candidate will work with a cross-functional team to deliver data-driven products and enhance decision accuracy and efficiency. | Post-train | 7 |
| Sr Lead Machine Learning Engineer This role focuses on productionizing and scaling machine learning applications and systems within a fintech domain. The engineer will be responsible for the design, development, deployment, and monitoring of ML models and infrastructure, with a strong emphasis on Python, Kubernetes, and cloud-based architectures. The role involves collaborating with data science and product teams, optimizing data pipelines, and ensuring the performance, availability, and responsible AI practices of ML systems. | ServeData | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, automating tests and deployment, collaborating with cross-functional teams, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code management, risk governance, and Responsible AI. | ServeData | 7 |
| Sr. Lead Machine Learning Engineer Senior Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, automating tests and deployment, collaborating with cross-functional teams, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code management, risk governance, and Responsible AI. | ServeData | 7 |
| Principal Associate, Data Scientist - Cash Flow Underwriting Data Scientist role focused on building machine learning models for cash flow underwriting in the fintech domain. The role involves leveraging advanced ML techniques to engineer predictive signals from banking and transaction data to forecast creditworthiness and customer behavior. It requires building models through all phases of development, from design to implementation, using technologies like Python, Spark, and AWS. | Post-train | 7 |
| Senior Machine Learning Engineer (AI Foundations) Senior Machine Learning Engineer focused on building and productionizing ML models and components at scale within an enterprise AI context. The role involves designing, developing, and implementing ML applications, including LLMs and agentic systems, with a strong emphasis on infrastructure, operational efficiency, and responsible AI practices. | ServeData | 7 |
| Senior Lead Software Engineer, Full Stack Lead the design and implementation of AI-native software engineering harnesses that use approved models, coding agents, and developer workflows in governed control-plane capabilities. Build agentic workflow orchestration systems that support planning, code generation, validation, retry loops, human checkpoints, and controlled promotion through delivery environments. Define and implement typed input/output contracts, schemas, metadata capture, and workflow state models that make agentic execution auditable and repeatable. Develop trace capture, observability, and debugging capabilities for AI-assisted engineering workflows, including prompt/output lineage, tool-call traces, model/runtime metadata, and failure analysis. Integrate evaluation results into CI/CD and release workflows, including quality gates for fidelity, build correctness, accessibility, security, performance, and human-review outcomes. | AgentServe | 7 |
| Lead Machine Learning Engineer (Manager IC) Lead Machine Learning Engineer role focused on building and deploying AI-powered solutions for Risk management within Capital One. The role involves designing, developing, testing, and deploying AI software components, including LLM inference, similarity search, guardrails, and agentic AI. It also includes fine-tuning models, managing production models, and optimizing data pipelines, with a strong emphasis on responsible and explainable AI. | AgentServe | 7 |
| Principal Associate, Data Science - Model Risk Office This role is in Capital One's Model Risk Office, focusing on defending the company against model failures and improving decision-making through models. The Principal Associate, Data Science will partner with cross-functional teams to identify and quantify model risks, lead teams of data scientists in building ML models to challenge existing production models, and contribute to the model governance framework. The role also involves leading model validation across various business domains and presenting identified risks to executives. The ideal candidate is innovative, creative, a leader, technical, and statistically-minded, with experience in model building, validation, and backtesting. | Ship | 7 |
| Senior Associate, Data Scientist Data Scientist role focused on building machine learning models for Capital One's Commercial Bank Team, involving forecasting, analytical tools, and model implementation. The role requires leveraging technologies like Python, AWS, and Spark to analyze large datasets and deliver data-driven solutions. | Post-train | 7 |