AI Engineer

• 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, FM Hosting, LLM Inference

Senior Lead AI Engineer, GenAI Platform

• 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.
• Implement evaluation frameworks, observability, and guardrails to ensure correctness, safety, and compliance of AI-driven systems.
• Design solutions for context management, memory, and retrieval-augmented generation (RAG) to enhance agent effectiveness.

Software Engineer 2 – AI Platform

• drive the architectural evolution and end-to-end delivery of core Knowledge features
• design scalable foundational systems
• take full ownership of product stability and customer success
• multiply the team's impact through technical coaching and mentorship
• champion Agile practices and continuous improvement
• apply a strong foundation in service-oriented design

Lead Software Engineer, Knowledge & AI Platform

• design and build AI‑powered solutions using Microsoft’s AI ecosystem, primarily Azure, GitHub, Copilot Studio, and Azure AI Foundry
• develop and integrate conversational copilots, retrieval‑augmented generation (RAG) experiences, and automation workflows into enterprise applications
• collaborate closely with product, engineering, security, and business stakeholders to deliver scalable, compliant, and measurable AI capabilities that support utility and energy customers
• Build and maintain AI solutions leveraging Azure AI Foundry, including model selection, prompt orchestration, evaluation, and deployment patterns, with a focus on reliable and customer‑centric outcomes
• develop and configure copilots using Copilot Studio, including topics, actions, plugins/connectors, and inclusive conversation design
• Implement AI integration patterns such as retrieval‑augmented generation (RAG), tool/function calling, and agent‑based workflows into applications and internal tooling
• create and maintain API services (for example, Azure Functions, App Service, and containerized services) to expose AI capabilities to enterprise business systems
• design and implement secure and accountable data access patterns for enterprise knowledge sources, including SharePoint, Azure SQL, and Blob Storage
• Use Azure DevOps and GitHub for source control, pull requests, code reviews, branching strategies, and release management in a collaborative and agile environment
• Build CI/CD pipelines that support automated testing, security scanning, and deployments to Azure
• Apply engineering best practices, including unit and integration testing, telemetry, error handling, performance tuning, and clear documentation
• Follow secure‑by‑design practices, including identity and access management (Entra ID), secrets management (Key Vault), and least‑privilege access
• Support responsible AI and governance requirements, including logging, auditing, evaluation, content safety, and prompt and security guardrails
• partner with security and compliance teams to ensure AI solutions align with enterprise policies and regulatory requirements common in the utilities and energy sector
• translate business needs into AI solution designs, user stories, and technical delivery plans that are practical and measurable
• work cross‑functionally with product, engineering, and business partners to define success metrics, validate outcomes, and iterate based on feedback and analytics
• provide guidance and enablement to teams adopting Copilot Studio and Azure AI Foundry patterns, fostering shared ownership and continuous learning

AI Developer

• Design, build, and deploy end-to-end AI-powered product experiences from concept through production.
• Architect and implement scalable AI systems leveraging LLMs, embeddings, multimodal models, retrieval systems, agent frameworks, and modern data infrastructure.
• Build production-grade multi-agent workflows and orchestration systems using frameworks such as LangGraph, LangChain, Mastra, and custom tooling.
• Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning.
• Build multimodal AI workflows that analyze and reason over images, creative assets, and visual datasets using modern multimodal LLMs, embedding models, and specialized tooling such as SAM2/SAM3.
• Create AI-assisted experiences for search, discovery, content generation, personalization, and creative workflows across Material Bank’s platform.
• Evaluate, refine, and improve AI-generated outputs for quality, tone, accuracy, and creative alignment through testing, iteration, and human-in-the-loop evaluation strategies.
• Partner closely with Product, Design, Engineering, Data, and Executive Leadership to identify high-impact opportunities and translate ambiguous ideas into production-ready AI capabilities.
• Make architectural decisions that balance speed, scalability, latency, cost, accuracy, and long-term maintainability.
• Continuously evaluate emerging AI technologies, models, frameworks, and workflows to identify opportunities that create meaningful business and user value.

Applied AI Engineer

• Architect, design, and implement full-stack AI applications using .NET and modern front-end frameworks.
• Implement AI/ML models and algorithms into .NET applications to improve business processes, analytics, and user experiences.
• Provide expertise in modern front-end development, with a preference for React, to deliver seamless and scalable user interfaces.
• Work within an Agile software development framework, contributing to sprint planning, iterative product delivery, and continuous improvement.
• Focus on delivering features and solutions that provide measurable value to end-users and stakeholders, particularly in commercial product settings.
• Partner with data scientists, analysts, and other developers to deliver secure, efficient solutions.
• Write clean, maintainable, and testable code following modern design principles and best practices.
• Ability to articulate architecture and design recommendations to leadership and contribute to the firm’s AI strategy.
• Stay current on AI advancements, .NET releases, and front-end frameworks to keep our technology stack modern and effective.
• Maintain technical documentation, versioning, and knowledge transfer materials.

Senior Full Stack AI Engineer

• Lead the design and evolution of agentic systems that participate directly in care delivery, helping coordinate care, engage patients, and execute meaningful portions of care workflows.
• Define and build architectural patterns for agent reasoning, tool use, memory, and human-in-the-loop collaboration.
• Develop both reusable platform capabilities and product-facing implementations, balancing long-term architecture with rapid iteration.
• Own complex problem spaces end-to-end — from system design and implementation through observability, evaluation, and continuous improvement in production.
• Partner closely with product managers, operators, and domain experts to translate complex real-world processes into reliable agent behavior.
• Establish standards and best practices for agent evaluation, safety, and operational reliability as these systems evolve toward greater autonomy
• Mentor engineers and influence technical direction across teams building agent-driven capabilities.

Principal AI Engineer

• Build AI-enabled applications
• Develop applications that leverage Azure OpenAI and Azure AI services
• Implement LLM-based workflows such as prompting, document analysis, and conversational experiences
• Support AI use cases such as document processing, automation, and intelligent workflow support
• Build integrations between AI services and enterprise platforms such as Salesforce, Genesys, and internal applications
• Work with APIs and services to enable AI-driven capabilities
• Implement workflows using tools such as Azure Functions, Logic Apps, or Power Automate
• Support AI-powered automation initiatives across operations and member/provider services
• Connect AI services with internal data sources including data warehouses, APIs, and operational platforms
• Assist with processing structured and unstructured data for AI use cases
• Partner with engineering, security, and data teams to deploy AI solutions
• Participate in testing, monitoring, and improvement of AI solutions in production

• Provide technical guidance for AI initiatives
• Design scalable AI systems and solutions
• Drive projects from proof-of-concept to production

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