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

• Build and nurture relationships with AI developers, data scientists, and technical communities worldwide
• Present at conferences, meetups, and industry events to showcase Intel's AI technologies and developer tools
• Develop compelling technical content including blog posts, tutorials, videos, and documentation
• Act as the voice of developers within Intel, influencing product roadmaps and developer experience improvements
• Drive adoption of Intel AI platforms by creating resources, samples, and best practices that accelerate developer success
• Engage with the developer community to position Intel as the platform of choice for AI development

AI Developer Evangelist

• 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

• Spearhead the development, deployment, and optimization of our Machine Learning models and Generative AI workflows
• Act as the bridge between technical execution and executive vision
• Lead the end-to-end lifecycle of ML and Generative AI models, including data structuring and implementation
• Deploy and maintain sophisticated AI applications
• Prepare decks and reports for leadership to translate complex technical milestones into clear business updates
• Maintain and scale a Python-based backend, ensuring seamless integration with our Next.js frontend

AI Engineering Lead

• Design, build, and operate scalable backend services and platform infrastructure that power Snowflake’s user-facing applications.
• Contribute across the full development lifecycle, from technical design and implementation to testing, deployment, and operational excellence.
• Collaborate closely with engineers, product managers, and cross-functional teams to deliver high-quality features and platform capabilities.
• Drive improvements to system reliability, scalability, performance, and developer productivity.
• Participate in architecture and design discussions, helping shape technical direction and engineering best practices.
• Build and operate cloud-native infrastructure and services running on Kubernetes.
• Improve deployment, observability, and operational workflows for services running in containerized environments.
• Debug and resolve complex production issues across distributed systems, Kubernetes infrastructure, and platform services.
• Build and enhance internal tools, including our coding agent/orchestrator, to create AI-assisted workflows that enable self-serve building of production features end-to-end on the platform through prompts.
• Contribute to a strong engineering culture through code reviews, knowledge sharing, mentoring, and operational ownership.

Senior Software Engineer – AI Platform

• Make marketing workflows more efficient through agentic AI
• Collaborate with Lifecycle Marketing (LCM) team
• Redesign repeatable workflows that marketing managers rely on
• Develop tools and automations to improve operational efficiency
• Share and incorporate builds across the AI Strategy & Innovation team

Forward Deployed AI Engineer – Lifecycle Marketing

• Embedded with a marketing team to build tools and automations
• Collaborate closely with marketers and share what you build
• Establish playbooks and patterns that scale good ideas across GMI
• Measure improvement in the way people work
• Rapidly prototype solutions based on feedback

Forward-Deployed AI Engineer

• Build and deploy AI-powered voice and messaging agents for healthcare and life sciences clients, end-to-end.
• Translate client workflows into real agent workflow design.
• Run technical sessions with clients' clinical, IT, and ops teams; converting needs into prompts, tools, retrieval shapes, and vector stores.
• Integrate with client data systems including CRMs, EHR/EMR platforms, specialty pharmacy systems, claims, and Rx data feeds and build the ingestion pipelines to support them.
• Write the production stack in TypeScript - agent runtime, routing, orchestration, evals; help build out the ML side in Python (training, embeddings, model registry); compose declarative guardrails & integrations within workflows.
• Engineer within HIPAA constraints: real-time API access to PHI (never stored at rest), de-identification pipelines, US-only data residency, encrypted recordings.
• Design and maintain databases (relational and caching layers) that support both real-time agent operations and compliance audit trails.
• Implement output guardrails ensuring agents remain informational and compliant with healthcare regulatory requirements.
• Codify per-client configuration patterns into reusable components so each new client onboards faster than the last.
• Collaborate with product, account management, and platform engineering to translate field learnings into platform improvements.

Principal Applied AI Engineer

• Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage.
• Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production.
• Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering.
• Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety.
• Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds.
• Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions.
• Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.

• 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

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

• Accelerating adoption of AI within the marketing team by building, managing, and optimizing AI-powered tools and workflows
• Deliver a steady cadence of milestones from marketing's AI-first backlog
• Help define and track role-based AI utilization goals across the marketing organization
• Manage the development and maintenance of MCP connectors across different marketing applications
• Set up centralized AI-based “skills” and governance frameworks for marketing AI usage
• Lead enablement efforts and host daily office hours to support the marketing team
• Share best practices across the team on Skills, Prompts, and Agentic workflows
• Design, build, and iterate on agentic AI workflows that automate and accelerate marketing processes
• Explore and implement Retrieval-Augmented Generation (RAG) customization where applicable

Marketing AI Engineer, Full-Time, 4 Month Contract

Lead AI Engineer, FM Hosting, LLM Inference

• Implement and maintain core services for the AI Data Lakehouse
• Build and support CI/CD pipelines
• Develop and test tool-execution environments and API interfaces
• Participate in on-call rotations and troubleshooting
• Work on the 'Context Fabric' to implement search and retrieval patterns
• Assist in managing cloud resources across AWS and Azure

Mid-Level Software AI Engineer, Context Engineering

• You will build at the intersection of Large Language Models (LLMs) and actionable code.
• Your goal is to move beyond "chat" and build systems that have the context and the "hands" to solve problems autonomously or in partnership with a user.

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