Top Tech Jobs & Startup Jobs in Charlotte, NC

Leads sales strategy enablement for Microsoft Security SME&C, Corporate, SMB, and partner teams. Develops sales learning paths, technical skilling programs, content, communities, and feedback systems. Measures enablement impact through KPIs, customer feedback, pipeline, adoption, usage, and renewal outcomes. Advises senior leaders, identifies readiness gaps, influences cross-functional stakeholders, and accelerates sales motions across enterprise and partner channels.

Lead strategy to define, measure, and evolve Solution Engineer and Cloud Solution Architect excellence for Microsoft Security. Use data, AI-powered analytics, and customer insights to identify performance drivers, design capability strategies, advise senior leaders, and drive field transformation. Build governance, risk remediation, and scalable enablement programs while partnering across engineering, product, sales, and enablement to measure impact and continuously improve outcomes.

Serve as a senior security solution architect advising enterprise customers on modern, cloud-native Microsoft security architectures. Design and validate solutions using Microsoft security products, remove technical blockers, act as SME on complex deployments, gather customer insights to influence product roadmap and map solutions to business outcomes, and support large-scale security and compliance projects.

Leads customer-facing cloud and AI data platform architecture on Microsoft Azure. Designs secure, scalable, resilient, and performance-optimized solutions; leads architecture sessions, proofs of concept, deployments, and modernization initiatives. Advises technical and business leaders, supports partners and sales teams, manages delivery risks, drives adoption and consumption, mentors colleagues, contributes to reusable frameworks and intellectual property, and influences product roadmaps through customer insights.

Leads customer-facing cloud architecture engagements across Azure AI, applications, infrastructure, and data workloads. Advises executives, designs secure and scalable solutions, guides implementation and operational excellence, resolves technical blockers, oversees strategic account delivery, and accelerates Azure adoption. The role also includes mentoring peers, contributing technical thought leadership, supporting proposals, maintaining cloud certifications, and helping enterprise customers adopt AI and modernize applications at scale.

Develop and execute Cloud and AI sales enablement strategies for corporate, SMB, and partner teams. Build learning paths, technical skilling programs, seller communities, and field connection mechanisms. Identify capability gaps, analyze KPIs and customer feedback, measure business impact, and use insights to improve sales motions, revenue growth, consumption, adoption, and renewal. Collaborate across sales, engineering, marketing, partner, and technical success organizations to scale enablement programs.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.

Lead hardware-software co-design for datacenter-scale AI systems, analyzing real workloads to drive architecture, performance, and cost improvements; prototype and productionize optimizations across accelerators, runtimes, compilers, interconnects, and distributed training/inference; mentor engineers and influence hardware and platform roadmaps.