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Vetted Site Reliability Engineering Professionals

Pre-screened and vetted.

CW

Mid-level Software Engineer specializing in Windows graphics performance and cloud automation

Redmond, WA6y exp
MicrosoftOregon State University

Graphics software engineer with academic robotics/HRI experience at Oregon State University under Dr. Heather Knight, leading a ROS+Python physical robot and Unity/C# VR system to study how motion/texture/collisions are perceived in VR (2 papers + thesis). Also built ROS-based Wizard-of-Oz TurtleBot study systems and multi-robot coordination experiments, plus industry experience with Docker/Kubeflow ML tooling and Azure DevOps CI/CD automation.

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RR

Director-level Engineering Leader specializing in SaaS, Cloud Migration, and Cybersecurity

Santa clara, CA8y exp
CiscoTexas Tech University

Senior engineering leader with experience at Cisco, Amazon, and startup Shopkick, operating at high scale (e.g., Secure Web Gateway handling ~40M QPS). Known for measurable impact across reliability and cost (85% efficacy improvement; Datadog spend cut from ~$500k/month to ~$15k/month) and for leading complex platform modernization (1-year monolith-to-microservices/event-driven migration with zero customer impact) plus compatibility-focused API design that cut device onboarding from a month to a day.

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SA

Shady Ashkar

Screened

Executive Technology & Engineering Leader specializing in SaaS, FinTech and HealthTech

Orange County, CA30y exp
OctaneUniversity of Notre Dame

Engineering leader (Octane) who drove business-aligned technology roadmaps and scaled a fully distributed engineering org from 35 to 150 in 9 months using a rigorous ownership model (OARP). Led a greenfield architecture for a new vertical (AWS Lambda/ECS, Python/JS/React) that delivered an MVP in 3 months while cutting per-service cloud OPEX from ~$300+ to ~$20/month, with 15+ years of remote-first leadership experience.

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GW

Senior Software Engineer specializing in SRE, cloud automation, and reliability tooling

Sunnyvale, CA11y exp
LinkedIn

Technically oriented builder focused on taking ambiguous LLM/agentic workflow needs from scoped problem definition to iterative prototypes, with emphasis on edge cases and customer feedback. Has hands-on experience debugging RAG/agent systems, including resolving a real integration issue involving search query quoting differences between manual search and an API, and is comfortable fielding deep developer Q&A in demos.

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TF

Senior Full-Stack Software Engineer specializing in SaaS, cloud-native systems, and AI/ML

Austin, TX11y exp
Amazon Web ServicesCollege of Charleston
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PK

Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps

San Francisco, CA5y exp
PerplexityConcordia University Wisconsin
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HM

Executive CIO/CTO specializing in AI-native SaaS, data platforms, and intelligent automation

Boulder, CO29y exp
RocketJobColumbia University
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SM

Mid-level Backend/Distributed Systems Engineer specializing in cloud observability and data ingestion

Seattle, WA6y exp
AmazonArizona State University
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XS

Mid-Level Systems Software Engineer specializing in distributed databases and data infrastructure

Los Angeles, CA4y exp
DigitBridgePurdue University
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AU

Principal Software Architect specializing in high-availability payments systems

San Jose, CA21y exp
PayPalDr. Babasaheb Ambedkar Marathwada University
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LK

Engineering Manager specializing in scalable platforms and FinTech SaaS

Fremont, CA14y exp
SalesforceGuru Gobind Singh Indraprastha University
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MG

Executive Transformation & Operations Leader specializing in Lean and Industry 4.0

Minneapolis, MN15y exp
IntelliShift LLCCapella University
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PA

Executive Software Engineering Leader specializing in digital transformation and omnichannel commerce

Chatsworth, CA18y exp
MGAEIIT Kharagpur
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DS

Executive innovation and portfolio leader in industrial, aerospace, and medical technologies

Mooresville, NC37y exp
Hanover VenturesUCLA
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EB

Senior Backend/Platform Engineer specializing in AWS-native data processing systems

Austin, TX11y exp
CiscoUniversity of Texas at Austin
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RJ

Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps

San Francisco, CA5y exp
PerplexityConcordia University Wisconsin
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DT

Executive Engineering Leader specializing in E-commerce, SaaS, and EdTech platforms

Delaware, USA22y exp
ChitChatShopOsmania University
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SS

Senior Technical Product Manager specializing in cloud database platforms on Azure

India12y exp
MicrosoftUniversity of Mumbai

Has hands-on familiarity with successful F2P mobile fighting games (e.g., Marvel Contest of Champions) and can articulate key retention/monetization systems like streak-based daily logins, gacha rewards, and limited-time events. While not having shipped a game directly, they have shipped web/mobile products integrated into gaming ecosystems and think in terms of live-ops health metrics and A/B testing for IAP offers.

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SJ

Sourabh Jain

Screened

Director of Software Engineering specializing in enterprise Data, ML & AI platforms

Bay Area, CA23y exp
RSA SecurityShri G. S. Institute of Technology and Science

Former Walmart Director of Software Engineering who left in March 2025 to build products for clients. Recently delivered an LLM/RAG-based UNSPSC classification solution for an MRO client using a multi-stage retrieval + web search + prompt-engineering workflow, and has led large-scale retail forecasting initiatives and high-severity cloud-migration incidents end-to-end.

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BK

Mid-level Full-Stack Software Engineer specializing in cloud microservices and AI integration

Jersey City, NJ3y exp
UberPace University

Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.

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JC

Jingyao Chen

Screened

Junior Backend/Platform Engineer specializing in AI microservices and cloud-native systems

Pittsburgh, PA2y exp
MeowyAICarnegie Mellon University

Cofounder at MeowyAI who shipped a production multimodal (vision/voice/text) AI task manager using Gemini, tackling real-world issues like hallucinations, tool-calling safety, and RAG-based preference memory. Also built a production multi-agent RAG system orchestrated with LangGraph (and contributes to LangChain), with strong emphasis on latency optimization, observability (OpenTelemetry), and rigorous testing/evaluation including A/B tests and adversarial prompting.

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MO

Mid-Level Software Engineer specializing in cloud-native distributed systems

Bellevue, WA7y exp
AmazonUniversity of Washington

Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.

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SB

Mid-level Backend & Reliability Engineer specializing in AWS, Kubernetes, and automation

New Mexico, US5y exp
MetaUniversity of North Carolina at Charlotte

Meta engineer focused on reliability/operations tooling who built a unified real-time health dashboard and scalable telemetry pipelines (AWS + Datadog) for thousands of devices. Also shipped an internal LLM-powered knowledge assistant using RAG over wikis/runbooks/logs with strong guardrails and a rigorous eval loop that drove measurable accuracy improvements via automated doc ingestion and embedding updates.

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JK

Jehanzeb Khan

Screened

Director-level Engineering Manager specializing in large-scale data and compute platforms

Sunnyvale, CA20y exp
AmazonInstitute of Business Administration

Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.

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