Top Tech Jobs & Startup Jobs in Charlotte, NC

Build and support complex features across C#/.NET back-end services and React front ends. Responsibilities include technical design, API and data modeling, secure and tested coding, production troubleshooting, code reviews, mentoring, documentation, and improving engineering practices. Collaborate with Product, QA, DevOps, Technical Leads, and architecture teams while balancing delivery, maintainability, performance, accessibility, observability, and security.

Design and deploy production AI/ML systems, including data pipelines, RAG systems, agentic workflows, model integrations, APIs, observability, evaluations, guardrails, and cost optimization. Collaborate with product, design, and client stakeholders while contributing to architecture, code reviews, testing, deployment, and technical strategy. Build reliable, secure, scalable systems using modern LLM and cloud technologies.

Implements a scientific software platform for enterprise R&D customers by migrating data, modeling experimental workflows, and supporting customer meetings. The role interprets scientific datasets, collaborates with chemists and researchers, supports enterprise rollouts, manages smaller accounts, and shares customer feedback with product and engineering teams. Candidates need STEM training, laboratory research experience, fluent French, technical curiosity, customer confidence, and strong data skills.

Lead the architecture and hands-on development of an AI-enabled data migration platform for legal software. Own extraction, transformation, orchestration, data quality, observability, and scalable go-live workflows. Partner with product management to define roadmap priorities, lead a focused engineering team, make architectural decisions, contribute code, and apply AI to migration assessment, mapping, validation, and automation.

Leads and grows a distributed software engineering team building a scalable cloud data and analytics platform. Owns architecture, data modeling, technical delivery, roadmaps, product collaboration, agile planning, quality standards, security alignment, vendor relationships, and engineering KPIs. The role remains hands-on with coding, code reviews, documentation, data architecture, deployment, and development of SaaS data products using Azure or AWS, Microsoft Fabric or Snowflake, and Power BI.

Leads multiple engineering teams delivering AI-powered data connectivity solutions. Owns execution, delivery, technical direction, operational reliability, and cross-team coordination. Develops engineering talent through coaching, hiring, and performance management while partnering with Product and Engineering leadership on priorities and organizational planning. Guides architecture, cloud-native development, incident response, SLOs, and AI-assisted software development practices. Requires strong Java/Kotlin expertise, cloud architecture experience, and technical judgment across distributed systems.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Manage and grow a product engineering team at a remote cloud cost-management software company. Lead prioritization, delivery, quality standards, recruiting, and cross-functional collaboration with product, design, and leadership. Help engineers ship customer-focused features spanning spend management, interfaces, data pipelines, visualizations, and application performance. Coach engineers, remove blockers, promote ownership, and guide tradeoffs between velocity and quality.

Design and build cloud cost-management capabilities, including forecasting, anomaly alerts, cost allocation, data pipelines, transformation models, visualizations, and reusable UI components. Own features end to end from planning through release while improving application reliability and performance. Collaborate closely with engineers and founding team members, contribute to technical design and roadmap decisions, and work pragmatically in a fast-moving distributed startup environment.

Build and maintain cloud infrastructure, automation, configuration management, and CI/CD pipelines. Operate Kubernetes, Helm, and Docker workloads across multiple public clouds; configure observability tooling; troubleshoot production systems spanning networking, databases, serverless functions, and caching; and participate in a 24/7 on-call rotation. The role also involves owning infrastructure projects end to end and collaborating across engineering and product teams.