Vetted Batch Processing Professionals

Pre-screened and vetted.

DB

Senior Engineering Leader specializing in FinTech infrastructure and cryptographic security

San Francisco, CA11y exp
MastercardUniversity of Colorado Boulder
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YW

Senior AI Engineer specializing in LLM systems and scalable backend platforms

San Francisco, CA8y exp
UberUniversity of Michigan
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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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EB

Senior Software Engineer specializing in backend, distributed systems, and computer vision

Arlington, VA13y exp
ImprobableCornell University
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BW

Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems

Seattle, WA10y exp
eBayUniversity of Illinois Urbana-Champaign
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DC

Senior Software Engineer specializing in AI backend platforms and FinTech systems

Hoboken, NJ11y exp
DoorDashOhio State University
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KD

Kanaad Deshpande

Screened ReferencesStrong rec.

Intern Software Engineer specializing in distributed systems and cloud infrastructure

San Francisco, CA1y exp
Sigma ComputingUC San Diego

Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.

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Sumer Joshi - Senior Full-Stack Engineer specializing in backend systems and AI applications in Remote

Sumer Joshi

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in backend systems and AI applications

Remote13y exp
MercorSanta Clara University

Candidate is deeply focused on AI-native software development, using a deliberate planner/implementer agent workflow with tools like Cursor, Claude, and Kimi. They also built a personal project called Config Proctor, an AI-agent-driven Terraform/AWS self-healing system that identifies infrastructure configuration gaps and proposes fixes.

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AS

Akshat Shah

Screened ReferencesStrong rec.

Entry-level Software Engineer specializing in full-stack and AI systems

Los Angeles, CA1y exp
Integrated Media Systems CenterUSC

Frontend-leaning full-stack engineer who described owning an artist search and detail experience across UI, backend integrations, and data modeling. They show practical strength in scalable React architecture, TypeScript safety, and performance tuning, with a product-minded approach to shipping 0→1 features quickly and iterating after launch.

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SM

SOURABH MISHRA

Screened ReferencesModerate rec.

Senior Software Engineer specializing in FinTech and distributed systems

Seattle, WA6y exp
AmazonUniversity of Cincinnati

Backend/AI engineer who has built a rule-service platform on AWS and evolved it into an agentic RAG system using LangChain, ReAct, tool calling, and LLM-as-judge review. Notable for combining heavy AI-assisted development with production safeguards like manual CR, CloudWatch monitoring, fallback strategies, benchmark testing, and user-feedback-driven model improvement.

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Amrita Pritam - Senior Backend Engineer specializing in distributed microservices and event-driven systems in Fremont, CA

Amrita Pritam

Screened

Senior Backend Engineer specializing in distributed microservices and event-driven systems

Fremont, CA10y exp
MicrosoftManipal Institute of Technology

Backend engineer with production experience building a high-scale notification pipeline (~20M/day) using Java/Dropwizard with Kafka and Azure Queue, including DLQ/poison-message handling and the outbox pattern for reliability. Also led a batch-based migration of Yammer Messaging user data from PostgreSQL to Azure Cosmos DB for global multi-region scale, addressing throttling and network failures via retries, escalation policies, and dynamic throughput tuning.

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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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JY

Senior Software Engineer specializing in backend APIs, search, and distributed systems

San Francisco, CA6y exp
SamsaraUniversity of Illinois Urbana-Champaign
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ZZ

Senior AI/ML Engineer specializing in Generative AI and NLP systems

McLean, VA9y exp
Capital OneUniversity of Pennsylvania
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KK

Executive engineering leader specializing in capital markets, risk systems, and FinTech

San Jose, CA28y exp
CognizantNYU
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RP

Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting

KS, United States12y exp
TargetKansas State University
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SJ

Intern Software Engineer specializing in systems, cloud, and security

1y exp
MicrosoftUniversity of Illinois Urbana-Champaign
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UY

Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines

California, USA6y exp
NetflixSacred Heart University
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CC

Intern Software Engineer specializing in full-stack and distributed systems

Seattle, WA0y exp
StarbucksHarvard University
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SR

Mid-level Full-Stack Python Engineer specializing in AI search and distributed systems

San Francisco, CA5y exp
RedditWebster University
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HS

Senior Software Engineer specializing in backend platforms and data systems

Bentonville, AR10y exp
WalmartGeorgia Tech
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TZ

Mid-level Data Engineer specializing in big data platforms and analytics infrastructure

New York, NY7y exp
MetaUniversity of Illinois Chicago
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JB

Senior Software Engineer specializing in backend distributed systems and FinTech

Austin, TX10y exp
AmazonUniversity of Texas at Dallas
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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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