Applied AI Solutions Architect

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Applied AI Solutions Architect based in the United States. Join an innovative technology environment where artificial intelligence is transforming complex business challenges into practical, measurable solutions. As an Applied AI Solutions Architect, you will bridge business strategy, solution architecture, and hands-on AI development to deliver impactful products and workflows. You will collaborate with product managers, analysts, customers, and engineering teams to identify opportunities and design solutions that address real-world needs. From intelligent agents and data retrieval systems to integrations and user interfaces, you will oversee the complete solution lifecycle, from initial discovery to production deployment. Using advanced large language models (LLMs) and AI-powered coding tools, you will accelerate development while maintaining high standards for security, reliability, and quality. This remote opportunity is ideal for a technically accomplished, business-minded professional who enjoys solving ambiguous problems, leading cross-functional initiatives, and delivering measurable business outcomes. Accountabilities - Business Problem Discovery: Partner with product managers, analysts, business sponsors, and customers to identify pain points, gather requirements, define problem statements, map user journeys, and establish clear acceptance criteria. - End-to-End Solution Ownership: Lead AI initiatives from initial concept through architecture, prototyping, development, deployment, and continuous improvement, ensuring solutions meet agreed business objectives. - AI Solution Architecture: Design fit-for-purpose solutions incorporating AI agents, tools, data retrieval, integrations, workflows, user interfaces, and human review processes where appropriate.

- Technical Design and Scalability: Develop architectures that balance performance, scalability, reliability, and cost while adhering to established security, data governance, and architectural standards. - Rapid Prototyping and Development: Build prototypes to validate assumptions, test technical approaches, and demonstrate value before progressing to full-scale implementation. - AI-Powered Engineering: Use Claude Code and other AI-assisted development tools to accelerate coding, automate repetitive engineering tasks, and build production-ready agents, connectors, integrations, and applications. Review generated outputs for correctness, security, and maintainability. - Cross-Functional Collaboration: Work closely with product, user experience, site reliability engineering, software development, quality assurance, and architecture teams to align technical solutions with business requirements. - Testing and Quality Assurance: Collaborate with quality assurance specialists and AI-powered testing agents to define test plans, validate functionality, evaluate output quality, and ensure production readiness. - Delivery Planning and Prioritization: Break complex initiatives into manageable phases, prioritize high-value work, coordinate technical decisions, and deliver incremental improvements against agreed timelines. - Performance Measurement and Optimization: Establish success metrics with business sponsors, monitor adoption, assess AI output quality, measure business impact, and refine solutions based on performance data and user feedback. - Technical Leadership: Facilitate decision-making, communicate architectural trade-offs, resolve disagreements constructively, and guide cross-functional teams toward effective implementation. - Responsible AI Implementation: Apply sound judgment when selecting AI-based approaches, ensuring appropriate human oversight, reliable outputs, and responsible use of organizational and customer data.