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

• Own and improve applied AI and ML systems that influence important business workflows.
• Design, train, and deploy models that generate optimal, fair, and explainable offers for claims, balancing business objectives with fairness constraints.
• Build and refine production workflows around generation, decisioning, retrieval, evaluation, and orchestration.
• Work on concrete systems such as position statement generation, offer engine improvements, open negotiation agents, and configurable rules or decisioning systems.
• Design and run experiments that improve model quality, workflow quality, and business outcomes.
• Partner with product, operations, and engineering teammates to define success metrics and translate messy real-world problems into buildable systems.
• Improve prompt, model, and workflow behavior through tight feedback loops and practical iteration.
• Contribute to the engineering quality of these systems, including instrumentation, testing, rollout safety, and operational visibility.
• Use AI as a force multiplier in your own work and help the team move faster by bringing strong AI-native habits.
• Balance speed and rigor in an environment where shipping matters and iteration is constant.

Senior Applied AI Engineer – Offer Engine

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• 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

• Full-Stack Product Development
• Architecture & Maintenance: Architect, develop, and maintain end-to-end applications powering AI-driven financial products.
• Backend & APIs: Build scalable backend services and APIs that support intelligent workflows and automated decision-making.
• UI Development: Create intuitive, high-performance user interfaces that surface complex insights and enable interactive experiences.
• Real-Time Systems: Design systems that support real-time communication between users, data sources, and AI components.
• AI & Data Integration: Partner with research and machine learning teams to integrate AI capabilities into production environments.
• Data Pipelines: Implement and maintain pipelines that ingest, process, and manage structured and unstructured financial data.
• Operationalization: Support the deployment and operationalization of AI-powered features and workflows.
• Engineering Excellence: Establish testing, observability, monitoring, and reliability standards across applications and services.
• Optimization: Optimize system performance, scalability, and maintainability.
• Technology Evaluation: Evaluate and adopt emerging technologies across AI, software engineering, and financial infrastructure.
• Continuous Learning: Stay informed on developments in large language models, agent frameworks, financial technologies, and modern web architectures.
• Best Practices: Contribute to technical discussions and help shape engineering best practices across the organization.

Full Stack AI Engineer

• Innovate in a Startup-Like Environment; developing AI-powered systems for accounting workflows
• Collaborate Across Functions; working with AI/ML engineers, frontend developers, product managers, and designers
• Help to build core AI interfaces; such as Conversations, Workspaces, Grid, and Knowledge Base
• Ramp up with a mentor; transitioning into contributing independently with a focus on quality, testing, and user experience

AI Engineer, New Grad

• 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

• Drive measurable business impact across H.I.G. Capital’s portfolio companies through AI-powered automation.
• Evaluate and select third-party AI platforms and vertical SaaS tools.
• Lead end-to-end implementation; configure workflows, integrate with existing enterprise systems.
• Define baselines and success metrics before every deployment.
• Train portfolio company teams on deployed solutions to enable independent operation.

AI Engineer – Forward-Deployed

• 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

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

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Partner with product, engineering, data science, and business teams to integrate AI into core offerings across SPGI
• Work with executive leadership to prioritize AI investments and resource allocation.
• Engage with external stakeholders (partners, vendors, academia) to foster innovation and collaboration.
• Establish best practices for AI model development, deployment, and monitoring.
• Ensure ethical AI use, compliance with regulations, and mitigation of bias and risks.
• Develop policies for data privacy, security, and responsible AI adoption.
• Oversee AI pilot programs and scaling of successful initiatives.
• Define KPIs to measure AI impact on business performance.
• Drive AI adoption across the organization through training and change management.
• Establish monitoring frameworks for AI system to system performance and drift analysis.
• Develop governance models for ethical AI agent deployment and compliance.

Associate Director – AI Engineering

Lead AI Engineer, FM Hosting, LLM Inference

• Lead the development, deployment, and ongoing optimization of AI and Generative AI solutions
• Implement engineering best practices to ensure scalability and uptime
• Track and report on platforms performance and availability metrics
• Be involved in all stages of the model development lifecycle from design to development, testing, and deployment
• Collaborate with software engineers, data scientists, and DevOps teams to design and implement technical designs and solutions
• Stay up-to-date with new technologies and industry trends

Lead AI Engineer

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

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