Top Tech Jobs & Startup Jobs in Charlotte, NC

Leads BPO sales strategy, enterprise client acquisition, strategic partnerships, solution development, market expansion, and revenue growth. Oversees sales team development, KPIs, budget allocation, cross-functional collaboration, and executive relationship management. Represents the company at industry events and drives customized outsourcing solutions for complex enterprise clients.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct comparative experiments, improve response quality, and create reusable prompt libraries. Translate business needs into prompting strategies while collaborating with AI engineers, trainers, product teams, and stakeholders. Document standards, support governance, research emerging techniques, and contribute to Generative AI implementation and continuous improvement.

Develop new business opportunities by prospecting, generating leads, building C-suite relationships, conducting client presentations, assessing needs, and closing complex consultative sales. The role involves creating tailored proposals and profitable programs, communicating value propositions, monitoring competitors, and collaborating internally on sales strategies. Frequent overnight travel is required for client meetings and presentations, with primary emphasis on new business rather than account management or team leadership.

Provides remote executive and personal administrative support, including calendar and meeting management, travel coordination, expense reporting, confidential information handling, correspondence, event planning, record keeping, bill payment, and project follow-up. The role requires proactive organization, sound judgment, excellent communication, and the ability to manage competing priorities independently while supporting senior leaders and operational initiatives.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Monitors and improves AI-powered systems by tracking performance, utilization, KPIs, incidents, and operational trends. Develops reports and dashboards, identifies efficiency opportunities, supports governance and compliance activities, documents procedures, and collaborates with AI engineers, product teams, business stakeholders, and operations leaders to resolve issues and improve adoption.

Leads business analysis for AI and automation initiatives, identifying adoption opportunities, developing business cases, gathering requirements, and aligning business needs with technical solutions. The role facilitates stakeholder workshops, supports solution design, testing, deployment, change management, governance, and benefits realization. It also advises executives, monitors outcomes, promotes business analysis standards, and mentors junior analysts.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.