Top Tech Jobs & Startup Jobs in Charlotte, NC

Leads enterprise CCaaS and customer experience transformation sales, from opportunity qualification through solutioning and deal closure. Develops CX strategies, architectures, roadmaps, and AI-driven use cases involving conversational AI, agent assist, analytics, and omnichannel engagement. Builds executive customer relationships, delivers technical and business presentations, manages complex stakeholders and partner ecosystems, and collaborates with architecture, data science, and engineering teams. The role requires 50% travel to customer sites and industry events.

Build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, RAG architectures, and GenAI infrastructure. Develop synthetic data workflows, automated data quality systems, and reliable cloud data platforms. Collaborate with architects, DevOps, and stakeholders, mentor engineers, lead prototypes, and apply AI solutions to insurance use cases while ensuring scalability, governance, monitoring, and operational resilience.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Design and lead large-scale AWS migrations (rehost/replatform), architect cloud infrastructure and platforms, automate migrations with IaC and scripting, troubleshoot OS/database issues, and implement hybrid networking and security best practices.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Senior technical sales and solutions leader responsible for architecting end-to-end AI solutions, advising strategic clients, shaping AI roadmaps, and leading technical sales cycles from discovery through production. The role requires expertise in generative and agentic AI, cloud architecture across GCP, AWS, and Azure, value-based commercial models, executive presentations, business analysis, and reusable AI asset development. The partner also mentors AI talent and contributes to go-to-market and company strategy.

Design and optimize enterprise data architectures and pipelines using SQL, Spark/PySpark, Python, Apache Iceberg, AWS, ClickHouse, and PostgreSQL. Build greenfield application and data solutions, optimize databases and Spark jobs, integrate data virtualization and governance tools, and support CI/CD and infrastructure-as-code practices. Experience with financial services workloads is valued, while telecommunications knowledge is preferred but not required.

Designs and maintains high-volume HL7 and FHIR ingestion pipelines, secure Epic and hospital integrations, data validation systems, and compliant healthcare data exchanges. Builds enterprise SaaS microservices using ReactJS, Python, and AWS services; optimizes distributed and hybrid-cloud environments; leads testing, code reviews, production troubleshooting, architecture documentation, and customer communications. Preferred experience includes Epic App Orchard, healthcare integration engines, clinical workflows, GenAI/ML, DevOps, data pipelines, and NoSQL.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.

Lead application migration and modernization initiatives to AWS, utilizing cloud-native practices and providing technical leadership for development teams.