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Vetted Microservices Architecture Professionals
Pre-screened and vetted.
Senior Front-End Developer specializing in Angular and enterprise web applications
Staff Full-Stack Engineer specializing in test automation, DevOps, and AI tooling
Senior Machine Learning Engineer specializing in LLMs and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Executive Technology Leader (CTO/EVP) specializing in product-led SaaS and data-driven platforms
Executive Technology Leader specializing in SaaS platforms and Generative AI
Senior Full-Stack Engineer specializing in cloud-native and AI-powered enterprise products
Mid-level Full-Stack Engineer specializing in Python, distributed systems, and FinTech
Senior Full-Stack Python Engineer specializing in trading and FinTech platforms
Senior Software Engineer specializing in cloud architecture and machine learning
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Senior Software Engineer specializing in cloud security and identity management
Thazim Banu Shaffee
Engineering Manager specializing in payments, risk, and high-scale distributed systems
“Engineering leader/player-coach on a risk core transaction platform (payments/branded checkout) who led major migrations from a monolithic stack to microservices, including API contract redesign and performance improvements (reported ~500ms latency reduction). Experienced running high-stakes production incidents (upgrade-related outage/degradation) end-to-end with RCA and rollout-process changes, and has accelerated delivery via documentation/tooling (audit sign-off cycle reduced from ~3 sprints to ~1).”
Jared Alessandroni
Executive CTO specializing in AI, cloud platforms, and scaling SaaS products
“NYC-based startup founder/CTO who sold products to Omnicom and Sprinklr, then built an AI-powered cultural insights engine inside Omnicom using AWS Lambda + ML to process ~1M items/day and reached ~$1MM ARR in year one. Former senior leader at Sprinklr managing 200+ people globally, delivering enterprise martech solutions with SLAs and high-reliability social data pipelines (Twitter firehose).”
Jiahua Huang
Intern Full-Stack Software Engineer specializing in web apps and cloud-native systems
“Backend engineer who scaled a food delivery platform by migrating from a single-service architecture to Spring Cloud microservices with an API gateway and Kafka-based event-driven order pipeline. Reported outcomes include ~50% latency reduction, stable ~2K RPS throughput, and 99.8% uptime, with strong emphasis on safe migrations (dual writes, canaries, schema versioning) and security (JWT/RBAC/Postgres RLS).”
George Liu
Intern Software Engineer specializing in full-stack, backend, and AI agent systems
“Backend engineer with Tesla experience who redesigned vehicle registration into a step-based, region-configured workflow across 4–5 microservices, enabling partial saves and reducing customer drop-off. Has hands-on experience scaling and securing Python/FastAPI APIs (OAuth2/JWT, CORS), migrating cold data from MySQL to MongoDB via Kubernetes CronJobs, and implementing RBAC/RLS with Supabase + Postgres.”
Kevin Lee
Senior Software Engineer specializing in scalable backend and platform systems
“Backend/data engineer with hands-on production experience across GCP (FastAPI microservices on Kubernetes) and AWS (Lambda, ECS Fargate, Glue). Has modernized legacy SAS batch systems into Python services with parallel-run parity validation, and has strong operational rigor in ETL reliability/monitoring plus proven SQL tuning impact (25s to <300ms, ~60% CPU reduction).”
Marcos Lopez
Senior Full-Stack Engineer specializing in cloud-native web apps and data pipelines
“Backend/data engineer with healthcare/telehealth domain experience, building patient appointment and data-processing systems on AWS. Has delivered production microservices and ETL pipelines (Flask/Celery, Glue/PySpark) with strong reliability/observability practices (JWT, retries/timeouts, Sentry/CloudWatch) and modernization experience migrating SAS workflows to Python services, including a documented 10min→30sec SQL performance win.”