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Vetted Matplotlib Professionals

Pre-screened and vetted.

VV

Mid-level Data Scientist specializing in Generative AI, RAG systems, and MLOps

Remote, USA5y exp
Enigma TechnologiesUniversity of Maryland, Baltimore County
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RK

Mid-level Software Engineer specializing in cloud, DevOps, and distributed systems

California, USA4y exp
University of Illinois ChicagoUniversity of Illinois Chicago
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SD

Mid-level MLOps/ML Engineer specializing in LLMs and financial risk modeling

United States4y exp
Northern TrustIllinois Institute of Technology
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NM

Mid-level Data Scientist specializing in ML, data engineering, and real-time analytics

Frisco, TX4y exp
OneDigitalUniversity of North Texas
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RM

Mid-Level Full-Stack Developer specializing in MERN and AR/VR applications

Indiana, USA4y exp
Purdue University NorthwestPurdue University Northwest
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JN

Intern Full-Stack Developer specializing in AI and web applications

San Rafael, CA2y exp
BioMarinSonoma State University
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JP

Mid-level Software Engineer specializing in AI and cloud-native data platforms

Overland Park, KS4y exp
APFMUniversity of Missouri
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B`

Mid-level Machine Learning Engineer specializing in LLMs, Generative AI, and MLOps

Albany, NY4y exp
Northern TrustUniversity at Albany
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AU

Senior Data Scientist and Machine Learning Researcher specializing in NLP, LLMs, and MLOps

Lubbock, TX9y exp
Texas Tech UniversityTexas Tech University
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JM

Mid-level AI/ML Engineer specializing in Generative AI and RAG assistants

USA4y exp
EPAMSacred Heart University
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KK

Mid-level Machine Learning Engineer specializing in healthcare and financial AI

Jersey City, NJ4y exp
Change HealthcarePace University
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US

Urvish Shah

Screened ReferencesStrong rec.

Mid-level Robotics Software & Systems Engineer specializing in ROS2 multi-robot systems

Buffalo, NY5y exp
University at BuffaloUniversity at Buffalo

Robotics software engineer with ROS2 multi-robot experience spanning decentralized signal source localization (LoRa RSSI on TurtleBot3) and a master’s-thesis project on collaborative object transportation with 4 robots. Strong in sim-to-real debugging—implemented noise modeling (RBF) and practical hardware/coordination fixes (CoG tuning, clock sync/flags) to make algorithms work reliably on real robots.

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SG

Sahil Gupta

Screened ReferencesStrong rec.

Junior AI Software Engineer specializing in LLM agents, RAG, and healthcare NLP

MA, U.S.A1y exp
AltiusUniversity of Massachusetts Amherst

Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.

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PR

Priyanka Ramesh

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React

Boston, MA2y exp
IpserLabNortheastern University

Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).

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TA

Tanweer Ashif

Screened ReferencesStrong rec.

Mid-level AI/ML Engineer and Data Scientist specializing in LLMs and MLOps

Buffalo, NY5y exp
University at BuffaloUniversity at Buffalo

Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.

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SC

Shashank Chauhan

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in AI/ML and cloud data platforms

Dearborn, MI3y exp
Data Science and Management Research LabUniversity of Michigan-Dearborn

ML engineer with hands-on experience taking a Gaussian Process Regression-based intelligent survey timing system from build to real-world deployment, including a 3-week RCT on 120 participants and measurable improvements (15% response rate, 23% data quality). Also served as a key technical resource at CData for customer-facing demos and debugging hundreds of production issues, bridging engineering with Sales and Customer Success.

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DM

Dara McNally

Screened ReferencesStrong rec.

Junior Computer Vision Researcher specializing in deep learning and object detection

Newark, DE2y exp
University of DelawareUniversity of Delaware

Robotics engineer who built and scaled a distributed perception stack on a Unitree Go1 quadruped, coordinating 5 Jetson Nanos and a Raspberry Pi to capture, aggregate, and stream multi-camera video in real time via UDP/GStreamer and custom ROS nodes. Also implemented a YOLOv9-based detection pipeline enhanced with Grad-CAM-driven selective image enhancement (e.g., MIRNet/UFormer) to improve real-time detections and robot reactions to visual stimuli.

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PY

Pallavi Yellisetty

Screened ReferencesModerate rec.

Mid-level AI/ML Engineer specializing in predictive modeling, NLP, and recommender systems

Bristol, PA4y exp
DermanutureUniversity of Texas at Arlington

AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).

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