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Top Tech Jobs & Startup Jobs in Charlotte, NC
Cloud • Information Technology • Professional Services • Software
Owner of secure, scalable infrastructure and CI/CD pipelines for financial trading systems. Integrate automated security testing, manage vulnerabilities, audit deployments, support DR/BC and compliance, and promote security-aware engineering practices.
Top Skills:
AWSCi/CdCloud SecurityContainer ScanningContainerizationDastDependency ScanningInfrastructure As CodeIso 27001Pci DssSastSoc 2
Cloud • Information Technology • Professional Services • Software
Design and fine-tune LLMs and RAG pipelines to parse regulatory texts and extract executable rules; build forecasting, classification, and risk-scoring models for supply chain; design robust data extraction and transformation pipelines (ERP/SAP, Data Lakehouse); integrate models via APIs; optimize performance and validate outputs to achieve production-grade reliability.
Top Skills:
BertData LakehouseFastapiFlaskGptJSONLlamaNoSQLNumpyPandasPythonPyTorchRestful ApisRetrieval-Augmented Generation (Rag)SAPScikit-LearnSQLTensorFlowXML
Cloud • Information Technology • Professional Services • Software
Design and implement end-to-end AI agent platforms: React/Next.js frontends with streaming LLM UIs, Python-backed agent orchestration, secure SAP integrations, vector DB-powered RAG, on‑prem deployment on GPU clusters, enterprise security (RBAC, PII/ITAR redaction), and observability. Build connectors, dashboards, and performant microservices for mission-critical financial and engineering workflows.
Top Skills:
AribaCss-In-JsDjangoDockerFastapiGoGrpcJavaJavaScriptKongKubernetesLocal Gpu ClustersMilvusNext.JsNginxNode.jsOdataPostgresPythonRagReactRestSap S/4HanaServer-Sent Events (Sse)SoapTailwind CssTypescriptVector DatabasesWeaviateWebsockets
Cloud • Information Technology • Professional Services • Software
Design and productionize LLM/NLP pipelines for regulation parsing and semantic search (RAG). Build forecasting, risk-scoring, and optimization models for supply chain. Implement MLOps: containerized deployments (Docker/Kubernetes), pipeline orchestration (Kubeflow/MLflow), inference optimization, and monitoring with retraining loops. Apply transformer models and prompt engineering for domain-specific tasks and convert natural language requirements into executable rule formats.
Top Skills:
BertDockerGptHugging FaceKubeflowKubernetesLangchainLlamaMlflowNumpyPandasPythonPyTorchScikit-LearnSQLTensorFlow
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