AI Engineer

Role : AI Engineer

Locatin : Charlotte NC

We are seeking a highly skilled AI Engineer with a Master's 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

• Master's 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