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Vetted FastAPI Professionals

Pre-screened and vetted.

PF

Senior Full-Stack Engineer specializing in AI, Web3, and scalable web platforms

Remote12y exp
AnthropicUniversity of Wisconsin–Milwaukee
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DW

Staff Full-Stack Engineer specializing in data engineering and real-time event platforms

Houston, TX10y exp
SalesforceMonash University
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YW

Senior Full-Stack Engineer specializing in cloud platforms, AI/ML, and blockchain

12y exp
AppleBaruch College
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AT

Senior Full-Stack Engineer specializing in cloud, real-time data, and web platforms

Mesquite, TX12y exp
Rocket LawyerUniversity of Texas at Austin
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MP

Senior Software Engineer specializing in cloud platforms, healthcare imaging, and scalable APIs

San Jose, CA10y exp
AmazonUniversity of Texas at Austin
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MP

Senior Software Engineer specializing in healthcare imaging and FinTech systems

San Jose, CA10y exp
AmazonUniversity of Texas at Austin
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IN

Staff Full-Stack & Platform Engineer specializing in cloud-native distributed systems

Houston, TX14y exp
AtlassianUniversity of Houston
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ML

Senior Software Engineer specializing in AI agents and cloud platforms

Louisiana, USA7y exp
NotionSanta Clara University
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EL

Senior Full-Stack Software Engineer specializing in Telehealth and FinTech

Santa Clara, CA11y exp
AmazonUCLA
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TB

Senior Software Engineer specializing in FinTech payments and scalable platforms

San Francisco, CA10y exp
StripeUniversity of Houston
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TC

Mid-level Software Engineer specializing in Python, distributed systems, and AI backend services

San Francisco, CA6y exp
OpenAIWebster University
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KC

Staff Software Engineer specializing in Healthcare SaaS and real-time systems

Seattle, WA11y exp
AmazonMonash University
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QG

Staff Software Engineer specializing in FinTech and scalable distributed systems

Menlo Park, CA12y exp
RobinhoodAugusta University
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HT

Senior Full-Stack Software Engineer specializing in large-scale streaming platforms

Seattle, WA10y exp
DisneyNYU
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MK

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

4y exp
NVIDIAFlorida State University
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NS

Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference

New York city, NY4y exp
PerplexityCleveland State University
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MG

Manaswini Gogineni

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development

San Francisco, CA2y exp
CiscoUniversity of Wisconsin–Madison

Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.

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YW

Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems

Taipei, Taiwan3y exp
NVIDIAUSC

Built a web-based interface that connects an internal bug system to an LLM for initial debugging and issue classification, aiming to boost QA and software engineer efficiency while balancing latency and accuracy. Worked as a one-person project and managed constraints like limited hardware and difficulty extracting team debugging context, relying on manager communication and rapid modeling to validate direction.

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BS

Engineering Manager specializing in AI/ML platforms and 0→1 product delivery

Cambridge, MA15y exp
ElsevierHarvard University

Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.

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NT

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference

San Francisco, CA6y exp
PerplexityUniversity of Nebraska Omaha

Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.

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KM

Kowshika M

Screened

Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety

Santa Clara, CA5y exp
NVIDIAOregon State University

AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.

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DS

Executive CTO and Founder specializing in AI platforms and hyper-scale SaaS

South San Francisco, CA26y exp
Deep OriginUC Berkeley

CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.

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NR

Nikhil Reddy

Screened

Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms

San Francisco, CA5y exp
NVIDIASaint Louis University

Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.

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