Data scientist

Data ScientistLocation: Charlotte, NC (Onsite) FulltimeMust Have Technical/Functional SkillsPrimary: Artificial intelligence/AISecondary: Python, NLP, Agile toolsExperience: 6 to 8+Roles & ResponsibilitiesJob Overview:6+ years of experience in Data Engineering, ML Engineering, AI Engineering, or Software Engineering.Strong proficiency in Python, SQL, and one additional programming language (Scala/Java/Go).Hands-on experience designing AI agents, toolchains, and agent frameworks (LangChain, Semantic Kernel, LlamaIndex, AutoGen, etc.).Strong understanding of LLM integration, function-calling patterns, prompt engineering, and agent orchestration.Experience deploying ML models using MLflow, Azure ML, SageMaker, Kubeflow, or similar.Proficiency in TensorFlow, PyTorch, Scikit-Learn, or equivalent ML frameworks.Experience building scalable data pipelines using Spark, Databricks, or similar technologies.Strong knowledge of RAG patterns, vector databases (FAISS, Pinecone, Chroma, Milvus, Redis), and embedding optimization.Experience with CI/CD pipelines, automated testing, code reviews, and deployment automation.Familiarity with containerization (Docker) and orchestration (Kubernetes).Experience integrating AI agents with enterprise APIs, microservices, and workflow systems.Familiarity with reasoning models, transformer architectures, and enterprise AI use cases.Knowledge of cloud platforms: Azure (preferred), AWS, or GCP.Experience with streaming technologies (Kafka, EventHub, Kinesis).Understanding of Responsible AI concepts, model fairness, bias detection, and safety filters. Experience with high-compliance environments (financial services, healthcare, or government).Industry certification will be plus (For example Azure/AWS/GCP Data or AI Engineering)The AI Agent Data/ML Engineer will design, develop, and operationalize AI agents, large-scale data pipelines, and machine learning solutions to support enterprise automation and analytics initiatives.This role requires strong engineering skills, expertise in LLM and agentic architectures, and the ability to deploy AI systems securely and reliably within a regulated environment.