Top Artificial Intelligence Jobs in Charlotte, NC

Handle inbound insurance leads, consult customers on Property & Casualty coverage, convert warm leads to policyholders, complete paid training and licensing, meet scheduled shift requirements, and maintain a professional W@H setup with provided equipment.

Support payer operations and transformation client engagements through research, data analytics, process improvement, and change management. Assist senior staff to develop and implement operational strategies, align stakeholders, measure performance, and drive efficiency within health services.

Lead finance transformation engagements using Oracle Cloud ERP and EPM; design solutions for budgeting, forecasting, reporting, and compliance; integrate RPA, ML, and analytics; manage stakeholders, coach teams, and deliver strategic outcomes across cross-border projects.

Lead and oversee Epic payer implementation programs, ensuring scope, schedule, and budget targets. Manage and mentor project teams, engage executive stakeholders, mitigate risks, drive operational and platform optimizations, and maintain high-quality client delivery standards across the implementation lifecycle.

Manage a $100M+ vehicle portfolio across Central/West territory, optimize resale decisioning and pricing, coordinate with auction partners, inspect and represent inventory, identify reconditioning/relocation opportunities, analyze market data, ensure SOP compliance, and drive processes to maximize sale proceeds and inventory turn while traveling frequently to auction locations.

Own the full sales cycle for the Charlotte territory: prospect, develop relationships, structure and negotiate complex deals, deliver proposals and presentations, manage pipeline and forecasting in CRM, collaborate cross-functionally on underwriting and operations, and leverage AI tools to improve workflows and drive net-new business.

Lead supplier quality improvement and SCAR program ownership. Drive data-driven assessments, RCA/RCCA investigations, predictive analytics and dashboards, and cross-functional supplier development to reduce defects, customer escapes, and delivery disruptions.

Lead value-led strategy for Financial Services (Insurance) accounts: develop industry-relevant value narratives, build business cases and KPIs, partner with account teams to drive pipeline and revenue, advise C-level stakeholders, and translate AI-driven transformation into measurable financial outcomes.

Design, build, and operate the GraphQL API platform that routes high-volume traffic. Deliver end-to-end projects, improve reliability and performance, own SLOs and incident response, mentor engineers, and collaborate with product teams to evolve platform APIs and tooling.

Manage an assigned book of business, drive revenue growth and renewals, upsell ancillary products, maintain client and broker relationships, support finalist sales presentations and onboarding, and coordinate internal teams to deliver client solutions and satisfaction.

Produce real-time closed captions by re-speaking into web-based software for live events. Work remotely on project-based assignments, complete training/qualification, perform pre-event checks, and ensure accessibility with clear speech, accurate grammar, and focused, timely captioning.

Lead corporate communications strategy and execution: own product and model launches, media and executive communications, narratives, social presence, and major company moments. Translate technical research and product advances into compelling stories for developers, enterprise buyers, partners, and analysts. Partner cross-functionally with CEO, CMO, product, brand, and demand gen to build credibility and influence in the voice AI ecosystem.

Lead end-to-end delivery of ML infrastructure and AI tooling programs, defining architecture and rollout strategies for training, serving, and monitoring. Coordinate research, engineering, product, and data teams to optimize inference cost/latency, build internal tools for experiment tracking and model versioning, and resolve bottlenecks to enable faster, scalable ML deployments.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.