AI ML Engineer

AI Engineer Job Title: AI Engineer Location: Charlotte, NC Interview: Face-to-Face / In-Person Employment Type: Contract / Contract-to-Hire Work Arrangement: Hybrid / Onsite as required Experience: 5+ Years Overall IT Experience; 3+ Years in AI/ML/GenAI Visa Status: USC, H1B, GC-EAD, H4-EAD only Job Summary We are seeking an experienced AI Engineer to design, develop, deploy, and support enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Large Language Model (LLM) solutions. The ideal candidate will have strong hands-on experience with Python, Generative AI, RAG, LLMs, Azure OpenAI, Azure AI Search, vector databases, AI agents, document intelligence, REST APIs, and cloud-native application development. The candidate will work closely with engineering, architecture, data, security, and business teams to build scalable AI solutions that integrate enterprise data and applications. Key Responsibilities Design, develop, test, deploy, and maintain production-grade AI/ML and Generative AI applications.Build LLM-powered applications using Azure OpenAI, OpenAI APIs, or other enterprise AI platforms.Design and implement Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge and document search.Develop AI-powered document processing, classification, extraction, summarization, and validation pipelines.Build intelligent AI agents using LangChain, LangGraph, Semantic Kernel, or similar frameworks.Implement agentic workflows including tool calling, orchestration, state management, context engineering, and structured outputs.Develop Python-based AI services, APIs, automation frameworks, and reusable components.Build and integrate REST APIs, microservices, and enterprise applications.Implement semantic, keyword, vector, and hybrid search capabilities.Develop embedding pipelines and integrate vector databases such as Pinecone, FAISS, Weaviate, ChromaDB, PostgreSQL/pgvector, or Azure AI Search.Perform prompt engineering, prompt evaluation, context optimization, and response-quality improvement.Implement document intelligence solutions using OCR, document extraction, classification, and entity recognition.Work with structured and unstructured enterprise data.Integrate AI solutions with SQL databases, APIs, data platforms, and enterprise applications.Develop model evaluation frameworks to measure accuracy, relevance, latency, cost, and reliability.Implement AI guardrails, security controls, monitoring, logging, and observability.Containerize AI applications using Docker and deploy them using Kubernetes/AKS or similar platforms.Participate in CI/CD implementation using Azure DevOps, GitHub Actions, Jenkins, or equivalent technologies.Troubleshoot production AI applications and improve performance and scalability.Collaborate with architects, developers, data engineers, cybersecurity teams, and business stakeholders.Participate in Agile/Scrum ceremonies, sprint planning, code reviews, and technical discussions.Create technical documentation covering architecture, implementation, APIs, AI workflows, and operational procedures. Required Technical SkillsProgramming Strong hands-on experience with PythonPython APIs and microservicesFastAPI or FlaskREST APIsJSONPydanticUnit testing and debuggingGit/GitHub Generative AI / LLM Large Language Models (LLMs)Generative AIPrompt EngineeringContext EngineeringStructured OutputsFunction Calling / Tool CallingAI AgentsAgentic AIMulti-step AI workflowsLLM evaluationAI guardrails RAG & Enterprise Search Retrieval-Augmented Generation (RAG)EmbeddingsSemantic SearchVector SearchHybrid SearchKeyword SearchQuery RewritingRerankingChunking strategiesMetadata filteringKnowledge retrieval AI / Cloud Platforms Hands-on Experience With One Or More Azure OpenAIAzure AI SearchAzure AI FoundryAzure AI Document IntelligenceAzure Cognitive ServicesAWS BedrockOpenAI APIsGoogle Vertex AI AI Frameworks Experience With One Or More LangChainLangGraphLlamaIndexSemantic KernelAutoGenCrewAI Vector Databases Skills: ai ml,vpython,vector,python,java