AI Engineer

Role : AI Engineer

Locatin : Charlotte NC

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