Vetted spaCy Professionals

Pre-screened and vetted.

AR

Intern AI/ML & Data Engineer specializing in deep learning, NLP, and cloud data pipelines

USA1y exp
TechMentee, Inc.Pittsburg State University

AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.

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ZA

Junior Full-Stack Software Developer specializing in cloud-native apps and data/AI

Nashville, TN1y exp
Belmont Data & AI CollaborativeBelmont University
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SR

Intern AI/ML Software Engineer specializing in NLP and model serving

Bangalore, India0y exp
Coincent.aiCalifornia State University, San Bernardino
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AT

Senior AI/ML Engineer specializing in FinTech and SaaS

Orient, OH8y exp
PaklogicsWalsh University
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MA

Senior Machine Learning Engineer specializing in NLP and production ML systems

Stafford, VA8y exp
OMW ConsultingInternational University
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SY

Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems

Bangalore, India3y exp
KreesalisRajasthan Technical University
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Manas Kumar Sherla - Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI in Charleston, IL

Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI

Charleston, IL2y exp
Eastern Illinois UniversityEastern Illinois University

Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).

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Built an automated ML/NLP document classification system for unstructured legal documents, combining classical models (TF-IDF + logistic regression/random forest) with entity resolution via fuzzy matching validated by precision/recall. Also implemented semantic similarity search using sentence embeddings stored in FAISS and improved matching by fine-tuning a transformer on domain-specific data and tuning similarity thresholds for fewer false positives.

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