| AI Engineer || Charlotte, NC || Hybrid || LOCALS ONLY at Charlotte, North Carolina, USA |
| Email: [email protected] |
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http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3422618&uid=e05968da499549e0bd02e297e6549089 From: Shubham Bansal, Technocraft Solutions [email protected] Reply to: [email protected] NO VISA || Locals only Role : AI Engineer Locatin : Charlotte, NC (Hybrid) We are seeking a highly skilled AI Engineer with a Masters degree in Computer Science, Artificial Intelligence, or a related field to design, develop, and deploy advanced AI/ML systems. This role is centered on building next-generation agentic AI solutions powered by retrieval-augmented generation (RAG), leveraging modern orchestration frameworks such as LangGraph and Model Context Protocol (MCP). The ideal candidate will have deep expertise in Python-based AI development and hands-on experience designing agent systems capable of reasoning, planning, tool usage, and executing complex multi-step workflows. A strong foundation in end-to-end RAG architectures, including Graph RAG, is required. Primary Skill: Artificial Intelligence/Machine Learning Secondary Skill: Python Tertiary Skill: Natural Language Processing Required Qualifications Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Strong proficiency in Python programming, with experience building scalable AI/ML systems. Hands-on experience with agentic AI frameworks, particularly LangGraph, and emerging standards such as Model Context Protocol (MCP). Strong experience designing and implementing advanced RAG architectures, including Graph RAG. Experience with LLM orchestration frameworks such as LangChain, LangGraph, and LlamaIndex. Proven experience deploying LLM-powered production systems. Design and implement advanced RAG pipelines using vector databases, embeddings, knowledge graphs, and hybrid retrieval strategies. Develop agentic AI systems using LangGraph, enabling dynamic task planning, reasoning, tool orchestration, and multi-agent workflows. Integrate Model Context Protocol (MCP) for standardized context sharing, tool interoperability, and scalable agent communication. Design memory systems and contextual state management for agent continuity and long-running workflows. Implement evaluation pipelines, prompt engineering strategies, and guardrails to ensure performance, safety, and reliability. Apply Model Risk Management (MRM) practices across the AI lifecycle, including model validation, explainability, bias detection, monitoring, and documentation. Strong experience with Python ML/AI frameworks such as PyTorch, TensorFlow, and Scikit-learn. Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search) and semantic retrieval systems. Deep understanding of agent orchestration patterns, including planning, reflection, tool usage, and multi-agent collaboration. Experience implementing Graph RAG using knowledge graphs and structured data integration. Expertise in memory architectures (short-term, long-term, episodic memory) in agent systems. Strong understanding of LLMOps/MLOps, including CI/CD, observability, monitoring, and performance optimization. Working knowledge of Model Risk Management (MRM) frameworks including governance, validation, and lifecycle controls. Familiarity with AI safety and alignment techniques, including guardrails, human-in-the-loop systems, and bias mitigation. Experience with model evaluation, benchmarking, and explainability tools . Proficiency with development tools such as GitHub, VS Code, JIRA, and modern engineering workflows. Desired Qualifications Experience working in an Agile development methodology; experience with RAG and LLM Intake Notes: Overview of the work being done Design and develop production-grade Python APIs/services Deploy and operate applications on OpenShift Partner with AI/ML engineers to productionize model capabilities into usable backend services Remediate vulnerabilities in: Python libraries/dependencies Container images OpenShift deployment configurations Primarily internal collaboration with cross-functional teams such as AI/ML engineers, UI developers, DevOps, and security/compliance stakeholders. Building Python-based microservices/APIs that expose AI/ML model functionality to downstream applications Deploying containerized applications to OpenShift and configuring manifests, services, routes, and secrets Integrating backend APIs with Angular-based front-end applications Performing remediation of security findings in Python dependencies and container images Automating deployment workflows using CI/CD pipelines aligned with OpenShift standards Thanks and Regards, Shubham Bansal US IT Recruiter Email: [email protected] Technocraft Solutions LLC Note: Please allow me to reiterate that I chose to contact you either because your resume had been posted to one of the internet job sites to which we subscribe, or you had previously submitted your resume to Technocraft Solutions. I assumed that you are either looking for a new employment opportunity, or you are interested in investigating the current job market. If you are not currently seeking employment, or if you would prefer, I will contact you at some later date, please indicate your date of availability so that I may honor your request. In any event, I respectfully recommend you continue to avail yourself to the employment options and job market information we provide with our e-mail notices. Keywords: continuous integration continuous deployment artificial intelligence machine learning user interface information technology North Carolina AI Engineer || Charlotte, NC || Hybrid || LOCALS ONLY [email protected] http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3422618&uid=e05968da499549e0bd02e297e6549089 |
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| 03:27 AM 04-Jun-26 |