AI Engineer

• Lead, mentor, and grow a distributed team of AI engineers
• Act as a player-coach: review architecture and contribute to design
• Shape and execute the technical roadmap for the AI Engineering team
• Design and deliver agentic systems end to end
• Build and evolve our internal AI platform
• Partner with leaders from Finance & Supply Chain Systems
• Collaborate closely with peer leaders in Enterprise Architecture
• Champion modern AI engineering practices
• Develop OKRs for the team and report progress
• Recruit world-class AI engineering talent
• Contribute to AI & Business Technology Engineering leadership team planning

Manager, AI Engineering – AI & Business Tech Engineering

• 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

• Support the design, development, and deployment of AI and ML solutions for the Long-Term Care program
• Work with Data Scientists and AI Engineers to help build and maintain ML and GenAI pipelines (data preparation, feature engineering, model training, and evaluation)
• Assist in deploying models into production environments using modern engineering practices
• Help monitor model performance, including accuracy, drift, and reliability, and support troubleshooting and improvements
• Contribute to both traditional ML use cases (predictive models, classification, forecasting) and GenAI use cases (summarization, document processing, RAG, automation)
• Develop and maintain APIs and services that integrate AI solutions into business workflows
• Follow established MLOps and LLMOps practices, including version control, testing, and documentation
• Support building observability tools such as logging, dashboards, and alerts
• Collaborate with cross-functional teams including engineering, data, business, and governance teams
• Ensure solutions follow enterprise standards for security, compliance, and responsible AI
• Continuously learn and apply new tools, frameworks, and best practices in AI, ML, and software engineering

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts
• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs
• Work closely with software developers to integrate LLM solutions into existing or new applications
• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units
• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness
• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection
• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base.

Senior GenAI Engineer

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts
• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs
• Work closely with software developers to integrate LLM solutions into existing or new applications
• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units
• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness
• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection
• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base

Lead AI Engineer, FM Hosting, LLM Inference

• Design and implement agentic AI systems capable of multi‑step reasoning, tool use, and autonomous task execution.
• Architect enterprise‑grade GenAI solutions, including RAG pipelines, embedding strategies, orchestration layers, and evaluation frameworks.
• Lead PoCs and pilots and scale successful solutions into production‑ready capabilities.
• Establish reusable architectural patterns, reference implementations, and technical standards for GenAI and agentic systems.
• Integrate AI solutions with existing platforms, data sources, and workflows to support adoption.
• Mentor team developers and contribute to raising overall GenAI and agentic AI maturity.
• Embed governance, security, evaluation, and monitoring considerations into solution design and deployment.

PharmSci Agentic and Generative AI Architect

• Define the strategic vision and roadmap for AI architecture practice across the enterprise, identifying high-impact opportunities for AI adoption
• Lead the design and development of robust, scalable, and secure AI architectures, and integration with existing IT infrastructure
• Serve as the top technical leader for AI architecture, guiding the selection of appropriate platforms, frameworks, and cloud technologies
• Build, lead, and mentor a multidisciplinary team of Architects fostering a culture of innovation and best practices
• Collaborate with architects from various functional domains, executive leadership, engineers, security specialists, and business stakeholders to provide direction for AI solutions and articulate complex technical concepts effectively to non-technical audiences
• Stay abreast of emerging AI advancements, such as Generative AI, agentic workflows, and drive innovation through prototyping and validating new technologies.

SVP, Head of AI Architecture

• Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability
• Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling
• Support development teams on model selection, training pipelines, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies
• Mentor engineers, analysts, and product teams on AI best practices.

AI Architect

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

Software Engineer 2 – AI Platform

• 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

• 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 Director, AI Engineering

• Implement our enterprise AI vision by architecting Generative AI and Agentic AI solutions
• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines, including data ingestion and document processing
• Increase model serving layers and high-throughput data processing pipelines
• Design and implement secure system architectures for all new AI products
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code.

Senior Agentic AI Architect – Engineer

• 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

• Develop and deploy agentic AI applications for clinical data interaction
• Build systems to extract patient phenotypes from clinical trial databases
• Construct AI and ML systems for data management and analysis
• Evaluate and monitor model performance in production
• Build pipelines for clinical trial databases

Applied AI Engineer, Clinical Informatics

• Architect and lead the development of agentic AI systems that automate and augment finance workflows.
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns.
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices.
• Develop and integrate AI services into internal applications and workflows.

See all jobs on JobTailor

Manager, AI Engineering – AI & Business Tech Engineering

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

AI Engineer

Director, AI Engineering – Agentic Platform

Senior GenAI Engineer

Senior GenAI Engineer

Lead AI Engineer, FM Hosting, LLM Inference

PharmSci Agentic and Generative AI Architect

SVP, Head of AI Architecture

AI Architect

Software Engineer 2 – AI Platform

QuantAI Engineer – Trading, Investment & Optimization

Senior Director, AI Engineering

Senior Agentic AI Architect – Engineer

Senior Manager, AI Lead – Semantic Layer

Applied AI Engineer, Clinical Informatics

Principal Applied AI Engineer, Finance