Build applications leveraging foundation models and advanced GenAI techniques such as GraphRAG.
Develop autonomous agents using modern agentic frameworks
Implement MCP integrations and Python-based full-stack solutions
Create scalable REST APIs and deploy AI workloads on major clouds
Apply LLMOps/MLOps best practices for CI/CD and monitoring
Work across teams, mentor others, and drive innovation in embeddings, knowledge graphs, and ontologies
What You’ll Bring with You:
Degree in CS, AI/ML, or related field
5+ years of AI/ML-focused software engineering
Production experience with LLMs and agentic systems
Exceptions versed in Python and associated AI libraries.
Experience with embeddings at scale, knowledge graphs, ontology extraction, and advanced GraphRAG
Full-stack skills (Python back end + modern front end)
Cloud deployment experience (AWS, Azure, or GCP)
Familiarity with LLMOps tools and strong problem-solving/communication skills
Position 2 # Applied AI Data Science
What You’ll Get to Do:
Perform statistical analysis, clustering, and probability modeling to drive insights and inform AI-driven solutions
Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence
Build NLP pipelines with a focus on NER, entity resolution, ontology extraction, and scoring
Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services
Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility practices
Collaborate with cross-functional teams to translate analytical findings into production-grade capabilities
Prototype quickly, iterate efficiently, and help evolve data science best practices across the team
What You’ll Bring with You:
Solid experience in statistical modeling, clustering techniques, and probability-based analysis
Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction
Strong NLP skills with practical experience in NER, entity/ontology extraction, and related evaluation methods
An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions (not purely theoretical)
Working knowledge of ML Ops basics, including experiment tracking and key model metrics
Proficiency in Python and common data science/AI libraries
Strong communication skills and the ability to work collaboratively in fast-paced, applied AI environments
Information
Locations Charlotte, NCPosition Open to Locals or from neighbouring statesIndustry IT StaffingStatus OpenJob Age 20 Day'sCreated Date 02/23/2026No.of Positions 2Duration 12Zip Code
