Vetted Flask Professionals

Pre-screened and vetted.

AM

Mid-level Full-Stack Engineer specializing in iOS, web, and distributed systems

Chicago, IL3y exp
University of ChicagoUniversity of Chicago
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TR

Mid-level Software Engineer specializing in full-stack and cloud platform development

Austin, TX5y exp
OracleCase Western Reserve University
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PB

Mid-level Software Engineer specializing in FinTech and AI-driven distributed systems

Sunnyvale, CA5y exp
Goldman SachsCalifornia State University, Chico
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NM

Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics

4y exp
New York Life
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AB

Executive Engineering Leader specializing in data platforms and SaaS

Los Angeles, CA13y exp
Los Angeles County Office of Education
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PA

Mid-level Full-Stack Developer specializing in React and Python (Django/FastAPI)

Toronto, Canada6y exp
The Home Depot
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GD

Senior Data Engineer specializing in cloud data platforms and real-time streaming pipelines

Rosemont, IL11y exp
Wintrust
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KK

Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI

New York, NY7y exp
JPMorgan Chase
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SK

Senior Full-Stack Java Developer specializing in microservices and Healthcare/FinTech systems

Seattle, WA5y exp
T-Mobile
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TS

Senior Full-Stack Developer specializing in cloud-native microservices (AWS)

Irving, TX8y exp
U.S. Bank
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AM

Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines

Philadelphia, PA11y exp
CoverMyMeds
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RS

Mid-level GenAI/ML Engineer specializing in LLMs, RAG, and agentic AI

Warrensburg, MO4y exp
Costco
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AJ

Anshul Joshi

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in distributed systems and GenAI

Austin, TX4y exp
University of Texas at AustinUniversity of Texas at Austin

Capgemini engineer with 4+ years building and deploying high-availability, low-latency fraud detection APIs and multi-cluster distributed systems for a Fortune 20 bank, including zero-downtime production rollouts and multi-layer (SQL/network/hardware) performance debugging. Also built a Python + OpenAI/LangChain LLM-powered grading workflow for Austin School for Women, cutting feedback time from 90 minutes to 5 minutes per submission for 200+ learners.

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KM

Kimberly Michela

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in web apps and developer tools

Toronto, Canada1y exp
Game of StreaksUniversity of Toronto

Frontend engineer who built the full frontend for gameofstreaks.com using React/Next.js with an emphasis on responsive design, accessibility, SEO, and performance profiling. Has experience refactoring legacy HTML/CSS/JS sites into reusable React components and shipping quickly with unit tests, CI/CD, and branch-based deployments; also collaborates with designers in hackathon settings (e.g., an AI interview practice app).

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Deeksha V - Mid-Level Software Engineer specializing in cloud data platforms and FinTech payments in Atlanta, GA

Deeksha V

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud data platforms and FinTech payments

Atlanta, GA4y exp
Delta Air LinesUniversity of Central Florida

Backend engineer focused on financial systems, having evolved payments and reconciliation platforms using Python/FastAPI and PostgreSQL with an emphasis on idempotency, validation, and consistency. Has led monolith-to-services migrations using feature flags and shadow traffic, and implements defense-in-depth security (OAuth2/JWT plus DB-level row security) for multi-tenant environments.

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KP

KrishnaVarshith Pabbisetty

Screened ReferencesStrong rec.

Mid-level Backend Software Engineer specializing in Spring Boot, microservices, and cloud-native AI

Bay Area, CA4y exp
TemenosSan José State University

Backend engineer with experience modernizing a large-scale procurement platform at Jio Platforms by breaking a monolith serving 35 business units into Spring Boot microservices, improving uptime and cutting report latency ~30%. Also built high-concurrency FastAPI systems (200ms at ~500 concurrent users) with strong security (JWT/OAuth2/RBAC), event-driven Kafka integrations, and reliability patterns like exactly-once delivery for ~1M monthly triggers.

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RO

Rafael Ortega

Screened ReferencesStrong rec.

Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems

Remote (Texas)8y exp
BlocUnitedNew Mexico Tech

Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.

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DC

dezhou chen

Screened

Mid-level AI Engineer specializing in LLM systems and GenAI products

Remote, USA4y exp
StealthUniversity of Illinois Urbana-Champaign

AI-focused product engineer working on LLM routing, prompt engineering, and multimodal API integrations at production scale. They describe improving system accuracy, latency, and token usage, fine-tuning an internal model to reduce third-party API dependence, and adding safety guardrails through prompt-injection testing and red-team evaluation.

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Nithin Reddy - Mid-Level Software Development Engineer specializing in distributed systems and event-driven architectures in Santa Clara, California

Nithin Reddy

Screened

Mid-Level Software Development Engineer specializing in distributed systems and event-driven architectures

Santa Clara, California4y exp
Goldman SachsCentral Michigan University

Built and maintained an internal JavaScript/React real-time event monitoring UI used by multiple Goldman Sachs teams (e.g., Private Wealth Management and Bulk Trading Systems). Focused on scaling performance under hundreds of events/sec—using profiling, memoization, batching, and debouncing—and paired it with strong internal documentation and disciplined incident diagnosis via synthetic load testing and logs/metrics.

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