Vetted Microservices Architecture Professionals

Pre-screened and vetted.

BB

Mid-level Full-Stack Software Engineer specializing in Python, React, and FinTech

New Jersey, USA3y exp
DatabricksPace University
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BS

Senior Full-Stack Engineer specializing in cloud-native enterprise applications and ServiceNow ITSM

Cleveland, Ohio16y exp
ProcoreCleveland State University
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MW

Senior Software Engineer specializing in cloud platforms and real-time collaboration

Remote13y exp
QuipMichigan Technological University
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SS

Director of Engineering specializing in cloud, microservices, and data-intensive platforms

San Ramon, CA31y exp
StealthIIT (BHU) Varanasi
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WK

Staff Full-Stack Software Engineer specializing in cloud-native microservices

Dallas, TX10y exp
JetBlueGeorgia Tech
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VS

Mid-level Software Engineer specializing in cloud-native platforms and healthcare systems

Dallas, TX3y exp
PlayStationUniversity of Texas at Dallas

Backend engineer with healthcare-domain experience building a security-critical RBAC identity/authentication/authorization microservice suite used across hospital imaging platforms (X-Ray, Ultrasound, etc.). Demonstrates strong security mindset (mTLS, cert hygiene, JWT, pen-testing collaboration) and pragmatic scaling/reliability practices (Nginx load balancing, Redis caching, automated tests, canary rollouts).

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MP

Senior Director of Software Engineering specializing in cloud-native microservices for streaming platforms

San Jose, CA20y exp
XperiAnna University

Engineering leader who drove TiVo IPTV’s client-facing API modernization from a monolith to AWS-based microservices (API Gateway, Lambda, EKS, Kafka, DynamoDB/RDS), including phased/blue-green production routing of millions of calls. Emphasizes org scaling through skill-based hiring, mentorship, and a you-build-you-run ownership culture, while balancing technical leadership with executive stakeholder communication and budgeting.

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LT

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

San Francisco, CA5y exp
City and County of San FranciscoSan Francisco State University

Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).

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CR

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

Austin, TX5y exp
AMDNortheastern University

Backend/AI engineer who built and scaled an internal AMD semiconductor manufacturing microservice platform (SMR), reworking a synchronous lot-request workflow into an event-driven RabbitMQ/Celery/FastAPI pipeline. Diagnosed and fixed peak-load reliability issues using deep observability and Kubernetes autoscaling, cutting notification latency back to sub-second and reducing duplicates via idempotency/DLQs. Also shipped an LLM-powered natural-language search with schema-constrained JSON outputs and guardrails, plus a plan-execute-verify Jira bug-resolution agent that can propose fixes and raise PRs under restricted permissions.

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Alexander Goldberg - Executive Technology & Product Leader specializing in AI/AR SaaS and cybersecurity in Los Angeles, CA

Executive Technology & Product Leader specializing in AI/AR SaaS and cybersecurity

Los Angeles, CA30y exp
ZugaraLomonosov Moscow State University

Engineering/technology leader with mission-critical experience at JPL NASA on the Mars Curiosity Rover, delivering an AI-driven navigation system designed for zero tolerance for mistakes and reportedly operating with no failures for 15+ years. Also led a monolith-to-microservices, cloud-native migration that improved scalability by 300% and cut deployments from days to hours, and is comfortable switching between executive fundraising/stakeholder communication and deep technical leadership.

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Byron Pineda - Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps in Pascagoula, MS

Byron Pineda

Screened

Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps

Pascagoula, MS10y exp
TuringMississippi State University

Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.

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Sarath Dunga - Mid-level Full-Stack Developer specializing in cloud microservices and AI/ML integration in Remote, USA

Sarath Dunga

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and AI/ML integration

Remote, USA4y exp
eBayArizona State University

Full-stack engineer (~3 years) with eBay production experience building and operating high-scale, event-driven Python microservices for order processing and AI-powered recommendations (Kafka/Redis/FastAPI on AWS with Prometheus/Grafana). Also delivered polished React+TypeScript analytics dashboards and designed high-concurrency PostgreSQL schemas with significant latency reductions. Recently built AI-agent orchestration and an interactive node-based requirements dashboard for Siemens Polarion via MCP servers, improving user interaction by ~17.8%+.

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Rishitha Madipelli - Mid-level Software Engineer specializing in cloud-native distributed systems and streaming data in Austin, TX

Mid-level Software Engineer specializing in cloud-native distributed systems and streaming data

Austin, TX7y exp
TeslaGeorge Mason University

Backend/product engineer with Tesla experience building and operating a real-time OTA update monitoring and fleet analytics platform at massive scale (telemetry from 3M+ vehicles). Delivered end-to-end systems across Kafka-based ingestion, TimescaleDB/Postgres analytics modeling, FastAPI/GraphQL APIs, and React/TypeScript dashboards, and handled production scaling incidents on AWS EKS during major rollout spikes.

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Vismay Patel - Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps in Berkeley, CA

Vismay Patel

Screened

Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps

Berkeley, CA7y exp
Kaiser PermanenteSan Francisco State University

ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.

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AD

Atharva Dani

Screened

Mid-level Full-Stack Engineer specializing in React and Go applications

California, USA3y exp
Arista NetworksBITS Pilani

Engineer with hands-on experience using AI coding tools like Claude Code and Augment in daily development, combined with real-world distributed systems work at Arista Networks. Built or worked on a Go-based platform that separated RBAC, audit logging, and provisioning into asynchronously communicating services, enabling scalable and independently evolving access-control workflows.

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Devika gade - Mid-level Full-Stack Developer specializing in FinTech and cloud-native applications in Remote, USA

Devika gade

Screened

Mid-level Full-Stack Developer specializing in FinTech and cloud-native applications

Remote, USA4y exp
PlaidChristian Brothers University

Full stack developer with strong implementation ownership across cloud deployments, integrations, and AI-powered support automation. They have put LLM/RAG workflows into production with measurable impact—cutting first response time by nearly 40%—and show unusual depth in debugging non-deterministic AI incidents, improving observability, and turning messy document inputs into reliable API-driven pipelines.

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JS

Jonas Shuai

Screened

Mid-level Full-Stack Software Engineer specializing in cloud, microservices, and React/Java

Menlo Park, CA3y exp
Mainspring EnergyUniversity of San Francisco

Software engineer with experience at PayPal and JPMC building large-scale onboarding/account setup systems using React/TypeScript with Spring Boot/Node microservices and Kafka. Also built an Ignition-based SCADA monitoring tool at Mainspring Energy that became the default for manufacturing/test engineers by aggregating real-time telemetry and historical test data.

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SS

Mid-level Business Data Analyst specializing in Financial Services and Healthcare analytics

USA4y exp
VisaGeorge Mason University

Full-stack engineer (~4 years) who has owned and shipped customer-facing SaaS onboarding and a role-based real-time analytics dashboard using TypeScript/React with a modular backend. Experienced in microservices with RabbitMQ and strong observability practices (correlation IDs, structured logging, queue metrics), and built an internal deployment tracker integrated with CI/CD that replaced manual spreadsheet/Slack processes.

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LL

Lisa Li

Screened

Director-level Engineering Leader specializing in SaaS, Cloud, and AI/ML delivery

Katy, Texas19y exp
Sainsbury'sThe Open University

Engineering leader who has led 100+ engineers at Sainsbury’s Tech and previously scaled an org from 6 to 60+ at AND Digital. Drove a high-impact modernization of a pricing/decisioning platform serving 1,700 stores—moving from batch monolith to real-time Kafka-based event-driven microservices with MLOps, IaC (Terraform), and zero-trust—delivering £18m+ annual profit uplift and 10+ deploys/day.

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SL

S Latha Naidu

Screened

Mid-level Software Development Engineer specializing in cloud-native backend systems

Seattle, WA5y exp
AmazonUniversity of Colorado Denver

Backend-focused engineer with experience at AWS building a global alarm processing platform (Python, Lambda/SQS/DynamoDB) handling traffic spikes and reliability issues; resolved duplicate alerts and latency under load by fixing hot partitions and enforcing idempotency. Previously at Cognizant, built Java/PostgreSQL backend workflows for healthcare dashboards using pre-aggregated summary tables, strong SQL optimization, and state-driven job orchestration with ELK-based observability and production guardrails.

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KC

Kevin Cruz

Screened

Senior Gen AI Engineer specializing in agentic LLM systems

Tempe, AZ15y exp
OpendoorUSC

Built and owned end-to-end production systems for a healthcare platform, including a predictive task recommendation feature (React + FastAPI + ML on AWS ECS) that cut backlog 20% and saved coordinators ~10 hours/week. Also productionized an AI-native RAG system (vector DB + LLM) delivering 40% faster query resolution, and led phased modernization of a monolithic FastAPI service into async microservices using feature flags and canary releases.

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Akhil Kunala - Mid-level Software Engineer specializing in backend systems and cloud-native FinTech in Seattle, WA

Akhil Kunala

Screened

Mid-level Software Engineer specializing in backend systems and cloud-native FinTech

Seattle, WA5y exp
AmazonUniversity of North Texas

Amazon engineer with 5+ years of experience who built an AI-assisted log investigation and triage workflow that cut debugging time by about 30% during on-call incidents. Combines observability tooling like CloudWatch and Splunk with Python, prompt engineering, and RAG-based diagnostics, and has practical experience orchestrating agentic AI workflows with a strong human-in-the-loop reliability focus.

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RK

Rutuja Kawade

Screened

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

Atlanta, GA3y exp
RakutenGeorgia Tech

Cloud infrastructure/product engineer with end-to-end ownership of cloud-native storage/observability products, including taking an internal CMS to Google Cloud Marketplace and scaling to ~40,000 deployments. Strong in Kubernetes-based platforms (Operators, microservices, RabbitMQ) and performance/scalability work (e.g., 200% cluster capacity increase) plus internal tooling that materially improved SRE/QA debugging and release velocity.

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