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Vetted Machine Learning Engineers in North Carolina
Pre-screened and vetted in North Carolina.
Mid-level Machine Learning Engineer specializing in MLOps and scalable ML pipelines
Vishnu Mukundan Thanikunathe Manoj
Junior AI/ML Engineer specializing in LLM agents, explainable AI, and computer vision
“Robotics/computer-vision engineer with industrial safety monitoring experience, building real-time pose estimation (TRTPose) and 2D-to-3D localization and optimizing pipelines to sustain 30+ FPS under heavy multi-entity load. Also brings edge-to-cloud distributed systems work (HoloLens + Google Vision/Translation) and production ML deployment experience using Docker/CI/CD across finance and edge camera environments.”
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Avinash Pancheneni
Mid-level Machine Learning Engineer specializing in fraud detection and LLM applications
“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”
Senior Machine Learning Engineer specializing in Generative AI, NLP, and MLOps
Mid-level Software & ML Engineer specializing in cloud data platforms and MLOps
Pravalika Kasojjala
Mid-level AI/ML Engineer specializing in LLM, RAG/GraphRAG, and fraud analytics
“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”
Mid-level AI/ML Engineer specializing in LLMs, RAG systems, and MLOps
Mid-level AI/ML Engineer specializing in fraud detection, risk modeling, and LLM/RAG systems
Mid-level AI/ML Engineer specializing in production ML, NLP/LLMs, and risk & automotive analytics
Intern AI Engineer specializing in LLMs, RAG, and multimodal generative AI
Senior Machine Learning Engineer specializing in AI platforms and cloud ML systems
Mid-level AI Software Engineer specializing in LLMs, RAG, and backend ML systems
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
Niharika Govinda
Junior Machine Learning Engineer specializing in NLP, data pipelines, and LLM workflows
“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”
Mid-level AI/ML Engineer specializing in LLM systems, MLOps, and real-time fraud detection
Sree Manasa Vuppu
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG
“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”