Vetted Amazon SQS Professionals

Pre-screened and vetted.

Aniket Singhal - Senior Software Engineer specializing in AWS-based distributed systems and FinTech platforms in Seattle, WA

Senior Software Engineer specializing in AWS-based distributed systems and FinTech platforms

Seattle, WA8y exp
AmazonBirla Institute of Technology, Mesra

Backend engineer with Amazon experience building large-scale, automated financial/accounting and pricing systems on AWS. Designed a fault-tolerant Step Functions + DynamoDB workflow platform handling 100K+ messages/sec to compute fair values and generate journal entries in under 3 seconds, and led safe API refactors using shadow mismatch testing. Also uncovered a major legacy pricing bug (tax vs non-tax swap) that cut mismatch rates from 5–10% to ~0.5% and materially improved price acceptance/business outcomes.

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Michael Kaufman - Mid-Level Full-Stack Software Engineer specializing in mobile and web platforms in Seattle, WA

Mid-Level Full-Stack Software Engineer specializing in mobile and web platforms

Seattle, WA5y exp
AmazonUC San Diego

iOS-focused engineer who led feature development for Amazon Books/Kindle (e.g., Series & Story So Far recaps, Kindle Memories) and introduced pure Swift packages while building sync and content download systems. Also has full-stack experience (React/TypeScript + Node with REST/GraphQL) and strong AWS operations (CDK/CI-CD, CloudWatch, canaries, autoscaling), plus founder experience at GLXY.ai shipping an early hardware MVP (weight sensors) under tight constraints.

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AA

Senior Full-Stack Python Developer specializing in cloud-native RAG and microservices

NY, USA6y exp
Google DeepMindUniversity of Saint Francis
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MS

Mid-level Software Development Engineer specializing in cloud platforms, data engineering, and LLM apps

Dublin, Ireland3y exp
AmazonGuru Gobind Singh Indraprastha University
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RB

Senior AI/ML Engineer specializing in scalable cloud ML systems

Seattle, WA14y exp
BCGPortland State University
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Yu Li - Mid-Level Software Development Engineer specializing in cloud microservices and retail systems in Reston, VA

Mid-Level Software Development Engineer specializing in cloud microservices and retail systems

Reston, VA5y exp
WalmartUniversity of Colorado Boulder
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RK

Senior Backend Engineer specializing in scalable AWS serverless and data pipelines

Sunnyvale, CA9y exp
AmazonUniversity of Oregon
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EW

Entry-Level Software Development Engineer specializing in AWS serverless and distributed systems

Sunnyvale, CA1y exp
AmazonUSC
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RL

Mid-level Software Engineer specializing in cloud infrastructure and backend systems

Vancouver, Canada4y exp
AmazonUniversity of British Columbia
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JD

Mid-Level Software Engineer specializing in distributed systems and AI agent platforms

Redmond, WA3y exp
MicrosoftSan Jose State University
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GD

Senior Full-Stack Engineer specializing in Healthcare and FinTech platforms

Minnetonka, MN9y exp
UnitedHealth GroupPrinceton University
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DZ

Senior Backend Engineer specializing in Healthcare & Cloud Platforms

New York, NY11y exp
DeloitteColumbia University
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VG

Mid-Level Full-Stack Software Developer specializing in AWS cloud and automation

USA5y exp
AmazonNYU
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GR

Senior Full-Stack Engineer specializing in scalable cloud platforms

Wayne, MI14y exp
AflacUniversity of Rhode Island
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VN

Mid-level Software Engineer specializing in backend systems, billing, and real-time data pipelines

CA, USA6y exp
StripeSoutheast Missouri State University
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SS

Intern Full-Stack Software Engineer specializing in distributed systems and cloud services

1y exp
AmazonDuke University
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YP

Senior Full-Stack & AI/ML Engineer specializing in FinTech and Healthcare IT

Remote12y exp
SnykUC Berkeley
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RT

Rhutwij Tulankar

Screened ReferencesStrong rec.

Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization

San Francisco, CA11y exp
RecruiticsRochester Institute of Technology

Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.

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Vigynesh Bhatt - Mid-level Software Engineer specializing in backend, cloud, and ML systems in Salt Lake City, UT

Vigynesh Bhatt

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend, cloud, and ML systems

Salt Lake City, UT4y exp
Goldman SachsBrigham Young University

Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.

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AC

Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision

Teaneck, NJ10y exp
AetrexColumbia University

Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.

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AR

Ali Rezafard

Screened

Director of Engineering specializing in cybersecurity SaaS platforms and cloud-scale backend systems

San Francisco, CA14y exp
ProofpointUniversity of Waterloo

Director of Engineering at Proofpoint for 8 years, leading architecture and integration of Java microservices within a detection platform. Demonstrates pragmatic delivery leadership—incurring short-term cost to meet launch deadlines, then systematically paying down technical debt and optimizing AWS spend—plus a disciplined, long-horizon approach to backward-compatible API/schema evolution across many dependent services.

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