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Vetted Unsupervised Learning Professionals

Pre-screened and vetted.

SH

Mid-level Backend Engineer specializing in distributed systems and industrial IoT

Indianapolis, IN4y exp
Purdue UniversityPurdue University Indianapolis

Backend/Python engineer focused on real-time sensor/IoT analytics: built dashboards and a high-throughput ingestion pipeline (MQTT -> Python worker -> TimescaleDB) with buffering, batch inserts, and validation. Strong Kubernetes + GitOps practitioner (Dockerized microservices, HPA, probes, ArgoCD) who has handled production incidents like CrashLoopBackOff under peak load and supported an on-prem analytics migration to AWS using shadow traffic and rollback plans.

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DS

Mid-Level Machine Learning & Backend Engineer specializing in computer vision and robotics systems

Nashville, TN5y exp
Universal LogicCal State Long Beach
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MM

Mid-level Machine Learning Engineer specializing in production ML, MLOps, and LLM retrieval systems

Dallas, TX6y exp
Nashville AnalyticsUniversity of Colorado Denver
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AB

Mid-level AI Engineer specializing in LLMs, RAG, and MLOps

Remote, USA4y exp
Credit AcceptanceAuburn University at Montgomery
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SW

Intern AI/ML Engineer specializing in RAG and adversarial robustness

Maharashtra, India0y exp
DeepCytes Cyber LabArizona State University
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DC

Junior Data Scientist specializing in LLMs, RAG, and multimodal AI

Bloomington, IN
Indiana UniversityIndiana University Bloomington
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AP

Mid-level AI/ML Engineer specializing in LLMs, RAG, and cloud AI infrastructure

San Diego, CA6y exp
Elevance HealthNorthwest Missouri State University
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DG

Mid-level Machine Learning Engineer specializing in MLOps, GenAI, and financial risk modeling

Texas, United States4y exp
PNCClark University
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RR

Mid-level AWS AI/ML Engineer specializing in cloud ML pipelines and NLP

Bristol, PA4y exp
DermanutureMontclair State University
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HP

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and scalable cloud ML

AZ, USA4y exp
DXC TechnologySacred Heart University
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SR

Junior Machine Learning Engineer specializing in LLM fine-tuning and AWS deployment

WA, United States1y exp
Vkare CorpUniversity of South Florida
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SP

Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps

San Diego, CA5y exp
Events.comSan Diego State University
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SG

Mid-level Data Scientist specializing in ML, NLP and forecasting

Catonsville, MD3y exp
EnigmaUniversity of Maryland, Baltimore County
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SB

Mid-level Software Engineer specializing in AI/GenAI and cloud-native backend systems

6y exp
Stardust Science LabsUniversity of Central Missouri
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PP

Mid-level AI/ML Engineer specializing in MLOps, fraud detection, and predictive analytics

USA6y exp
Northern TrustSacred Heart University
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NZ

Principal AI Architect & Data Engineer specializing in GenAI, agentic systems, and MLOps

Dallas, TX4y exp
American Techno Solutions Inc.University of the Cumberlands
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AM

Mid-level Applied AI Engineer specializing in LLMs, Prompt Engineering, and RAG

United States (Remote)4y exp
SprinklrOklahoma City University
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DN

Junior Full-Stack Software Engineer specializing in web, mobile, and AI-enabled collaboration tools

Blacksburg, VA2y exp
Virginia TechVirginia Tech
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PG

Mid-level AI/ML Engineer specializing in cloud AI, MLOps, and NLP

Washington, USA4y exp
iLink DigitalFlorida Atlantic University
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JJ

Mid-level Data Engineer specializing in cloud data platforms and real-time pipelines

Denton, TX5y exp
Real DynamicsUniversity of North Texas
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NJ

Senior AI/ML Engineer specializing in Generative AI and LLMOps

Washington, DC10y exp
Clarion Tech
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SR

Swarag Reddy Pingili

Screened ReferencesStrong rec.

Junior AI/ML Software Engineer specializing in LLM agents and RAG systems

Frisco, TX2y exp
WorldLinkUniversity of Texas at Arlington

AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.

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