.png){kind=logo}
Vetted Machine Learning Engineers in North Carolina
Pre-screened and vetted in North Carolina.
Bhargav Gopal
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.”
Varsha Ravula
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.”
Mitesh Singh
Junior Software Engineer specializing in AI agents and LLM integrations
Jimmy Li
Senior Machine Learning Engineer specializing in Generative AI, NLP, and MLOps
Venkata Mahalakshmi Vishnumolakala
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.”
Sai Vikas Reddy Yeddulamala
Mid-level AI/ML Engineer specializing in LLMs, RAG systems, and MLOps
Sai Boorlagadda
Mid-level AI/ML Engineer specializing in fraud detection, risk modeling, and LLM/RAG systems
Sai Ravi Teja Grandhisila
Mid-level AI/ML Engineer specializing in production ML, NLP/LLMs, and risk & automotive analytics
Jeevan Vikkurty
Mid-level Generative AI Engineer specializing in LLMs and RAG
Manikantan Srinivasan
Intern AI Engineer specializing in LLMs, RAG, and multimodal generative AI
Venkata Lakshmi Krishna Tejaswi Gudimetla
Mid-level AI Software Engineer specializing in LLMs, RAG, and backend ML systems
Sandesh Mahajan
Mid-level AI/ML Engineer specializing in GenAI, NLP, and MLOps on cloud
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.”
Shanmukha Deety
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.”