Vetted C Professionals

Pre-screened and vetted.

YD

Senior Software Engineer specializing in Unity, multiplayer networking, and AI/LLM integration

San Francisco, CA7y exp
UberStanford University
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AG

Senior Software Engineer specializing in cloud security and identity management

Chicago, IL8y exp
AmazonUniversity of Illinois Urbana-Champaign
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MT

Executive engineering and product leader specializing in hardware, IoT, and automotive innovation

Boston, MA11y exp
YazakiMIT
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NM

Mid-level systems researcher and engineer specializing in distributed storage and operating systems

Pittsburgh, PA8y exp
Carnegie Mellon UniversityCarnegie Mellon University
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Rodolfo Garcia - Senior QA Test Analyst specializing in game and service testing in Los Angeles, CA

Rodolfo Garcia

Screened ReferencesStrong rec.

Senior QA Test Analyst specializing in game and service testing

Los Angeles, CA11y exp
NetflixLos Angeles Mission College

Game QA/automation tester with experience at Blizzard, Bossfight Entertainment, and Netflix, spanning manual-to-automation transitions, Selenium/C# UI automation, and CI/CD nightly reporting via TestRail. Known for an end-user-driven test strategy, strong defect isolation (including crash dumps), and cross-functional test planning that influenced multiplayer UX/design decisions.

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AS

Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning

Pittsburgh, PA1y exp
McKinsey & CompanyCarnegie Mellon University

McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.

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YL

Yutao Liu

Screened

Senior Software Engineer specializing in backend services and full-stack web platforms

Palo Alto, CA8y exp
AmazonUC Irvine

Project lead who partners with PM and customers to gather requirements, adjust project plans, and deliver new functionality that drives customer satisfaction and revenue. Has experience building features end-to-end and presenting successful technical demos to engineering and management audiences; no stated experience with LLM/agentic systems.

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SC

Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG

CA6y exp
Scale AIUniversity of Texas at Arlington

Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.

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KR

Executive Engineering Leader specializing in cloud services, distributed systems, and networking

Cupertino, CA29y exp
AmazonUniversity Visvesvaraya College of Engineering

Amazon engineering leader (15+ years) targeting Senior Manager/Director roles, with deep ownership of contact-center latency and reliability initiatives. Shipped a global production improvement cutting call latency 30–40% and led a complex Citrix SDK integration, including incident response and a backward-compatible rollout strategy to protect existing customers while enabling new features.

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PP

Entry-level Supply Chain & Test Engineer specializing in warehouse automation and robotics

1y exp
Procter & GambleMichigan State University

P&G operator who is also building and selling an AI receptionist (voice agent) SaaS for healthcare/service clinics, using EHR + calendar API compatibility to target accounts and letting the Voice AI run parts of the demo to prove value. Has already closed and deployed to two clients in the last two months, with production impact via reduced front-desk overhead and automated scheduling/FAQs, and brings a structured, scalable deployment/process mindset from global WMS rollouts.

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DJ

Daming Jiang

Screened

Intern Software/AI Engineer specializing in LLM fine-tuning and agentic RAG systems

0y exp
AT&TCornell University

Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.

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CY

Staff Software Engineer specializing in distributed systems and platform architecture

Aldie, VA15y exp
ProviUniversity of Maryland, College Park

Built a production LLM-powered data ingestion workflow at Provi, an online alcohol marketplace, to clean and match millions of distributor inventory items against a product catalog. Their experience is strongest in applying LLMs to real-world, large-scale data operations with AWS Glue, S3, batching, API integration, human review, and drift detection.

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LC

Lewis Chen

Screened

Mid-Level Software Engineer specializing in cloud infrastructure and data systems

Sunnyvale, CA4y exp
GoogleUC Berkeley

Backend engineer who helped redesign and refactor Forma’s backend during an app rewrite, emphasizing modularity, maintainability, and A/B testing support while delivering feature parity on a quarter-long timeline. Led a careful database migration using parallel databases with schema differences, validating integrity via staging and SQL checks, and has experience debugging subtle computer-vision overflow edge cases.

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CH

Chengzhu He

Screened

Staff/Principal Cloud Infrastructure Engineer specializing in Kubernetes and OpenStack

14y exp
TikTokShanghai University

Platform/backend engineer focused on Kubernetes at scale: built a Java control-plane service for multi-region cluster provisioning/monitoring/upgrades using Kafka-driven async workers, and solved peak-load provisioning failures by eliminating blocking I/O and dynamically scaling consumers. Also shipped an LLM-assisted Kubernetes troubleshooting/remediation feature that pulls Prometheus logs/metrics into prompts and uses guardrails (confidence thresholds + human-in-the-loop) to prevent risky actions.

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Jingfei Xu - Intern/Junior Software Engineer specializing in AI/ML and cloud-based systems in Mountain View, CA

Jingfei Xu

Screened

Intern/Junior Software Engineer specializing in AI/ML and cloud-based systems

Mountain View, CA0y exp
AmazonCarnegie Mellon University

Embedded/robotics software engineer with Hyundai Motors experience who owned an AI-driven perception validation pipeline using a Transformer-based approach to generate stable synthetic in-cabin audio for autonomy/ASR testing, cutting downstream testing time by 50%+. Has hands-on ROS integration (IMU sensor streaming, inference, control nodes), MQTT-based distributed messaging, and cloud/container deployment experience (Docker, Node/Express, AWS, CI/CD).

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LW

LEQUAN WANG

Screened

Intern Applied Scientist / ML Engineer specializing in NLP and conversational AI

Seattle, WA0y exp
AmazonUC Irvine

LLM/Conversational AI engineer who built a production multi-turn dialogue system using LoRA fine-tuning on LLaMA, cutting training compute/memory by 90%+ while maintaining low-latency inference via quantization and streaming generation. Experienced in orchestrating end-to-end ML workflows with Prefect/Airflow/Kubeflow (including hyperparameter sweeps and W&B tracking) and improving agent reliability through benchmark-driven testing, shadow-mode rollouts, and stakeholder-informed guardrails.

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KK

Kevin Kyi

Screened

Intern Machine Learning Engineer specializing in RAG systems and AWS cloud infrastructure

Pittsburgh, PA1y exp
BlueFoxLabs AICarnegie Mellon University

Internship at BlueFoxLabs building and deploying an AI/ML RAG system for a biopharma client on top of LibreChat, including an AWS Textract ingestion pipeline and PGVector retrieval deployed to AWS EKS. Demonstrated production-minded scalability work by moving from a vertically scaled EC2 setup to a horizontally scaling Kubernetes/EKS deployment, using CI/CD to safely incorporate requirement changes like tabular document data.

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JH

Jiahua Huang

Screened

Intern Full-Stack Software Engineer specializing in web apps and cloud-native systems

1y exp
AmazonUniversity of Illinois Urbana-Champaign

Backend engineer who scaled a food delivery platform by migrating from a single-service architecture to Spring Cloud microservices with an API gateway and Kafka-based event-driven order pipeline. Reported outcomes include ~50% latency reduction, stable ~2K RPS throughput, and 99.8% uptime, with strong emphasis on safe migrations (dual writes, canaries, schema versioning) and security (JWT/RBAC/Postgres RLS).

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Sara Rubacha - Engineering Manager specializing in databases and distributed systems in Weston, FL

Sara Rubacha

Screened

Engineering Manager specializing in databases and distributed systems

Weston, FL21y exp
UKGUniversity of Buenos Aires

Aspiring founder exploring an AI automation startup focused on automating processes involved in building companies. Not yet developed specific use cases or raised capital, but describes a clear plan to validate ideas through use-case research, building a pilot, and testing with early customers; not familiar with the VC/accelerator landscape yet.

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KL

Kevin Lee

Screened

Senior Software Engineer specializing in scalable backend and platform systems

Los Angeles, CA8y exp
Riot GamesUniversity of Waterloo

Backend/data engineer with hands-on production experience across GCP (FastAPI microservices on Kubernetes) and AWS (Lambda, ECS Fargate, Glue). Has modernized legacy SAS batch systems into Python services with parallel-run parity validation, and has strong operational rigor in ETL reliability/monitoring plus proven SQL tuning impact (25s to <300ms, ~60% CPU reduction).

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SM

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

San Francisco, CA5y exp
Scale AIConcordia University Wisconsin

Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.

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SW

Entry-Level Software Engineer specializing in systems, networking, and ML

Atlanta, GA0y exp
AtlassianGeorgia Tech

Robotics software candidate with hands-on experience building controllers for an Autonomous Underwater Vehicle, including dual-PID control in Python with state-space modeling and a planned path to LQR. Developed ROS nodes for odometry-based localization, waypoint planning, and control command publishing, validated through a custom Gazebo/ROS simulation workflow with control-metric-driven testing. Also worked on F1Tenth simulation and scan-matching localization (PL-ICP), with additional cloud deployment experience using Docker/Kubernetes and CI/CD.

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Sergey Pustovit - Director-level Data Platform & Analytics Engineering Leader specializing in distributed systems in Irvine, CA

Director-level Data Platform & Analytics Engineering Leader specializing in distributed systems

Irvine, CA31y exp
SentinelOneNational University "Odessa Maritime Academy"

Entrepreneurially minded builder focused on proving architecture concepts via minimal demo prototypes for marketing. Has hands-on experience improving an A/B experimentation framework by interviewing stakeholders, identifying system limits and bottlenecks, and defining success criteria to scale experimentation and speed up analysis.

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