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Vetted Prompt Engineering Professionals

Pre-screened and vetted.

RR

Rutvi Rathod

Screened

Junior Full-Stack Software Engineer specializing in AI and web applications

San Jose, CA1y exp
FreelanceChhotubhai Gopalbhai Patel Institute of Technology

LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.

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KM

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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ML

Junior Graphic Designer & Social Media VA specializing in AI-assisted content and ads

Bongabon, Philippines2y exp
Zenith VenturesCollege for Research and Technology

Marketing creative designer producing polished static ads and lightweight social videos, primarily using Photoshop, Canva, and CapCut (no Figma yet). Emphasizes scalable template-based workflows for high-volume output, strong visual hierarchy for clarity, and remote async collaboration with rapid feedback cycles; shared portfolio on Behance.

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AA

Entry Machine Learning Engineer specializing in quantitative finance and DeFi

Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.

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MA

Entry AI Engineer specializing in machine learning, computer vision, and data mining

Houston, TX
University of DamascusUniversity of Damascus
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