AI Engineer

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including foundation model training and large language model inference.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, and PyTorch.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large scale production AI systems.
• Contribute to the technical vision and the long-term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer, Gen AI Platform

• 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.
• Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team.
• Foster a culture of learning and staying abreast of the state-of-the-art in AI.

Senior Manager, AI Engineering – Gen AI Platform Services

• 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.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Senior Lead AI Engineer, Inference Optimization, FM Hosting, AI Platform

Lead AI Engineer, FM Hosting, LLM Inference

• Responsible for researching, designing, developing, and implementing Artificial Intelligence (AI) solutions
• Work with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders
• Identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support
• Help establish and mature AI capabilities across the ICAM ecosystem by leveraging machine learning, generative AI, predictive analytics, natural language processing, and intelligent automation technologies
• Support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment
• Ensure compliance with DoW security requirements and responsible AI principles
• Design, develop, test, and implement AI and machine learning solutions supporting enterprise ICAM objectives
• Identify opportunities to improve identity governance, access management, onboarding processes, and operational workflows through AI-driven capabilities
• Develop predictive analytics, anomaly detection, behavioral analysis, and risk-scoring models using identity and access management data
• Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services
• Evaluate emerging AI technologies and recommend solutions applicable to ICAM, Zero Trust, and cybersecurity use cases
• Develop proof-of-concepts, prototypes, and pilot programs to validate AI capabilities before enterprise adoption
• Provide technical leadership and subject matter expertise regarding AI technologies and industry best practices.

AI Engineer – ICAM

Lead AI Engineer – GenAI Platform, AI Foundations, LLM Core, Agentic AI

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

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Lead AI Engineer

Senior Director, AI Engineering

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large scale production AI systems
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Design and develop autonomous AI agents capable of reasoning, planning, and executing tasks independently.
• Build and orchestrate multi-agent workflows using modern AI frameworks and tooling.
• Develop AI-enabled applications using large language models and retrieval-augmented generation (RAG) architectures.
• Integrate AI agents with APIs, databases, enterprise systems, and external services.
• Build conversational and human-in-the-loop interfaces for AI systems.
• Develop observability, tracing, and evaluation pipelines for AI workflows and agent performance.
• Containerize and deploy AI applications using Docker-based infrastructure.
• Collaborate with software engineers, cybersecurity teams, researchers, and stakeholders to deliver scalable AI capabilities.
• Maintain CI/CD pipelines and source control workflows using GitLab.
• Optimize prompts, memory systems, context handling, and agent decision-making.
• Research and prototype emerging agentic AI techniques and frameworks.

AI Developer

• The AI Engineer supports system-specific implementations that embed AI/ML and agentic automation to accelerate metadata identification, extraction, enrichment, and documentation.
• Design AI-assisted metadata harvesting & enrichment.
• Implement agentic/GenAI workflows for metadata documentation.
• Own the metadata harvesting plan.
• Human-in-the-loop steward validation.
• Responsible automation scoping.
• Build and maintain scalable pipelines (metadata-focused).
• RAG / knowledge integration for semantic discovery.
• Metadata quality metrics & validation controls.
• Technical testing & verification.
• Mentor and elevate engineering rigor.

AI Engineer – Architect

• Design, develop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

• 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
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One

Senior Lead AI Engineer – Gen AI Platform Services

• conduct statistical analyses on business processes using ML techniques
• train, test, deploy, and maintain models that learn from data
• own and define the direction of critical solutions by applying best-fit ML algorithms and technologies
• collaborate with data engineers, data scientists, solutions architects, and MLOps engineers to deliver world-class solutions

See all jobs on JobTailor

Senior Lead AI Engineer, Gen AI Platform

Senior Manager, AI Engineering – Gen AI Platform Services

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Senior Lead AI Engineer, Inference Optimization, FM Hosting, AI Platform

Lead AI Engineer, FM Hosting, LLM Inference

AI Engineer – ICAM

Lead AI Engineer – GenAI Platform, AI Foundations, LLM Core, Agentic AI

QuantAI Engineer – Trading, Investment & Optimization

Lead AI Engineer

Senior Director, AI Engineering

Lead AI Engineer

Senior Manager, AI Lead – Semantic Layer

AI Developer

AI Engineer – Architect

AI Engineer – Mission Innovation Lab

Senior Lead AI Engineer – Gen AI Platform Services

Applied AI Engineer