Vetted FAISS Professionals

Pre-screened and vetted.

DY

Entry-Level Software Engineer specializing in backend services and applied ML

Orlando, FL0y exp
University of North FloridaUniversity of North Florida
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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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SR

Entry-level Generative AI Developer specializing in LLM agents and RAG systems

Patna, India1y exp
Mobirizer ServicesLakshmi Narain College of Technology
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SO

Junior Software/AI Engineer specializing in GPU-accelerated HPC and machine learning

Wichita Falls, Texas4y exp
Midwestern State UniversityMidwestern State University
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RD

Junior Machine Learning Engineer specializing in Agentic RAG and Document AI

Durgapur, West Bengal, India2y exp
CAPSITECH IT SERVICES PVT. LIMITEDHaldia Institute of Technology
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Kumar Manik - Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp
Elevate LabsBarkatullah University

Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.

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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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HF

Intern Machine Learning Engineer specializing in NLP, RAG, and time-series forecasting

New York, NY0y exp
Gao TekVirtual University of Pakistan
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