Role Description Lead innovation and knowledge strategy for CX (Customer Experience) Learning & Enablement by defining knowledge architecture, implementing AI and workflow intelligence solutions, and driving automation across the content lifecycle. Combine product, data and learning expertise to deliver modern, searchable, and integrated knowledge experiences that reduce friction for learners, support agents and partners. Core Responsibilities: - Define and own the knowledge architecture and taxonomy strategy for CX learning and enablement content (including canonical content models, metadata standards, and governance). - Design and implement generative AI/RAG solutions, intelligent search, conversational assistants, and contextual learning surfaces that integrate with product workflows and frontline tools. - Own strategy and execution for content lifecycle automation and integrations (CMS ↔ LMS ↔ KM ↔ product docs ↔ translation pipelines), improving authoring, review, and publishing velocity. - Act as technical product owner for integrations and tooling (content APIs, search, knowledge base platforms, LLM infra), coordinate with Product Engineering and P&E for secure, scalable deployments. - Drive experimentation and pilot programs to validate AI/automation use cases (e.g., auto-summary, auto-tagging, content recommendations, test generation). Evaluate vendors and open-source approaches; manage POC lifecycle. - Implement measurement frameworks for knowledge efficacy: search success, answer relevance, RAG precision/recall, content reuse, time to publish, and impact on case deflection and support outcomes. - Build data governance practices for content/AI (bias, privacy, provenance, feedback loops). - Collaborate with Curriculum Development, Learning Business Partners, Adoption & Value, and Support to ensure innovation projects map to clear business outcomes. Qualifications - 8+ years’ experience in knowledge management, product/technical program management, or AI/data product roles with at least 3+ years leading teams or programs. - Practical experience deploying enterprise AI/search/KM solutions (LLM/RAG, vector search, knowledge graphs, conversational AI) and implementing integrations between LMS/CMS/KM systems. - Strong technical literacy: APIs, data modeling, metadata/taxonomy design, and vendor evaluation for AI/KM tooling. - Experience working with engineering/product teams to bring technical solutions to production. Requirements - Bachelor’s degree in technical field, information science, or equivalent experience. - Hands-on experience with LLMs, vector databases, RAG approaches, and tools. - Experience in SaaS/HR tech or with support and digital adoption use cases. - Familiarity with content localization pipelines and enterprise taxonomy management. - Advanced degree in information science, AI, or related field. Competencies - Technical product leadership - Innovation & experimentation - Systems thinking - Data-driven decision making - Stakeholder influence across technical and non-technical partners - Risk and governance orientation Benefits The pay range for this position is $129,500 to $186,100. The actual base pay offered may vary depending on skills, experience, job-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG’s benefits and rewards at UKG Benefits .

Agentic AI Engineer Charlotte, NC Type: Direct-Hire Category: Information Technology Reference ID: 10076878 Shortcut: http://addisongroup.gosnaphop.com/rRqvYY Title: Agentic AI Engineer Location/Schedule: Charlotte, NC with a hybrid weekly schedule (3 Days Onsite/2 Days Remote) Status: Salaried, Exempt Full-Time Position Summary A rapidly growing healthcare services organization is seeking its first Agentic AI Engineer to help shape and deploy enterprise-wide AI capabilities. This newly created role offers a unique opportunity to work directly with executive leadership and business stakeholders to understand operational processes, identify automation opportunities, and deliver production-ready agentic AI solutions. Unlike traditional architecture-focused positions, this role is centered on implementation, experimentation, and execution. The ideal candidate is a builder who enjoys moving quickly from concept to deployment and thrives in environments where requirements are evolving and ambiguous. Ideal Candidate Profile We are seeking someone who: - Partners effectively with executives and business leaders to solve operational challenges. - Understands complex operational data, large semantic models, Power BI semantic models and reporting environments, and modern AI tooling to investigate business questions. - Can take an ambiguous business problem and build a working AI solution that generates actionable insights. - Is comfortable working with tools such as Codex, Claude Code, Cursor, and modern agent frameworks. - Understands enterprise systems, integrations, and business processes. - Focuses on delivery and execution rather than high-level solution architecture. - Has experience developing agents that reconcile insurance data, summarize clinical or operational records, automate procurement variance analysis, and trigger workflows in enterprise systems. - Understands the importance of protecting PHI and sensitive employee information. Key Responsibilities AI Agent Development - Design and develop AI agents utilizing LLMs and agent frameworks. - Build multi-agent workflows for enterprise use cases. - Implement reasoning, planning, memory, and tool-use capabilities. - Develop reusable agent templates and autonomous workflows integrated with enterprise applications. - Incorporate PHI/HIPAA-aware development practices including RBAC, audit logging, least-privilege access, prompt injection defense, evaluations, and human approval mechanisms for high-risk actions. Enterprise Integration Connect AI agents with: - CRM platforms such as Salesforce and HubSpot. - Healthcare and dental EMR systems. - Databases, internal APIs, and SaaS applications. - Power BI semantic models, datasets, reports, and embedded analytics environments. - Retrieval-Augmented Generation (RAG) solutions leveraging company knowledge repositories. - Authentication, authorization, and secure function-calling mechanisms. Platform Engineering - Develop and maintain agent orchestration workflows. - Monitor agent reliability, performance, and operating costs. - Build testing, evaluation, observability, and logging frameworks. - Optimize prompts, workflows, and tool utilization. Delivery & Collaboration - Partner with business teams to identify automation opportunities. - Translate operational requirements into agentic AI solutions. - Partner with Finance and business leaders to augment existing reporting processes, including Power BI-based analyses and operational insights, with agentic AI solutions. - Rapidly prototype and iterate on AI-driven workflows. - Document implementation standards and mentor junior developers on best practices. Required Qualifications - 3–5+ years of software engineering experience. - Strong Python development background. - Experience building applications utilizing LLM APIs such as OpenAI, Anthropic, or Google. Experience with: - Healthcare or dental EMR platforms. - LangGraph - LangChain - CrewAI - AutoGen / Microsoft Agent Framework - MCP-enabled systems - API integrations - SQL and relational databases - Power BI, including semantic models, DAX, datasets, and report integrations. - AWS, Azure, or GCP cloud environments. Preferred Qualifications - Vector databases including Pinecone, Weaviate, or pgvector. - Retrieval-Augmented Generation (RAG) architectures. - Agent evaluation frameworks. - CI/CD and DevOps practices. - Docker and Kubernetes. - Knowledge graph implementation. - Prompt engineering and model tuning. - Experience leveraging Power BI as an input, output, or orchestration layer within AI-driven workflows and enterprise automation initiatives. Success Metrics - Number of production AI agents delivered. - Workflow automation hours saved. - Agent reliability and task completion rates. - Reduction in manual processes. - User adoption and satisfaction. - Cost efficiency of deployed solutions. - Time-to-deployment for new agent solutions. Compensation & Benefits - $140,000–$175,000 base salary plus 10–15% annual bonus. - Hybrid work arrangement with three days per week onsite in Charlotte, NC. - Comprehensive benefits package including medical, dental, vision, life insurance, wellness programs, 401(k), paid holidays, competitive PTO, paid parental leave, and annual incentive opportunities.

• This role owns the full lifecycle of conversational AI assistants across our Group brand portfolio, spanning digital and voice channels. • You will design, build, test, launch, monitor, report on and iterate hybrid generative assistants, then expand proven approaches across additional brands once value has been demonstrated. • Design conversational flows and hybrid generative assistant experiences for digital and voice channels, aligned to brand tone of voice and customer need. • Build assistants using enterprise conversational AI and generative AI tooling, including intent design, dialogue management and integration with existing digital systems. • Test assistants for accuracy, safety and tone consistency before launch, covering scenario and edge case testing. • Launch assistants into live, customer-facing environments across relevant brands. • Monitor live performance against defined success metrics, identifying accuracy, containment and customer experience issues as they arise. • Report on performance using auditable, evidence-based metrics rather than self-reported or anecdotal measures. • Iterate assistant design and logic based on live performance data and customer feedback. • Expand proven use cases across additional brands and channels once value is demonstrated. • Apply consistent design standards across a multi-brand portfolio, adapting tone and content to individual brand requirements. • Collaborate with technology, operations, compliance and brand stakeholders to align conversational AI work with wider business priorities.

• Own the end-to-end performance of Ashley Digital's AI customer experience, defining customer intents, desired outcomes, success metrics, and conversation strategy across all AI workflows. • Design, test, and continuously improve conversational experiences through prompts, response logic, routing, escalation strategies, and system instructions, ensuring every interaction feels natural, helpful, and true to the Ashley Digital brand. • Balance automation with customer experience by determining when AI should resolve customer needs independently and when human support delivers the best outcome. • Monitor customer sentiment, conversation quality, and behavioral analytics to continuously improve tone, clarity, empathy, resolution quality, and overall customer experience. • Analyze AI performance using KPIs such as CSAT, One-Touch Resolution, Total Resolution Time, containment, escalation, retention, conversion, and AOV to identify optimization opportunities and prioritize the roadmap. • Build and maintain a continuous improvement framework by leveraging customer sentiment, QA findings, conversation analytics, operational data, and experimentation to optimize AI performance and customer outcomes. • Conduct recurring conversation reviews to identify friction points, underperforming workflows, emerging customer trends, and knowledge gaps, translating insights into actionable improvements. • Partner closely with Customer Support, Product, Engineering, QA, Operations, CRM, Legal, and Brand teams to align AI experiences with evolving customer needs, business priorities, and operational processes. • Own and maintain the enterprise knowledge base, ensuring it serves as the trusted source of truth for both AI and human support. • Establish scalable knowledge management practices, including taxonomy, metadata, governance, content lifecycle management, templates, and authoring standards that keep information accurate, AI-ready, and easy to maintain. • Continuously improve the knowledge base using conversation insights, customer feedback, and operational changes to ensure content remains current, relevant, and effective. • Communicate performance trends, business impact, and strategic recommendations to stakeholders through clear reporting, dashboards, and data-driven insights.