Vetted Agile Professionals

“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”

“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”

“Engineering leader who drove Lightroom Mobile’s roadmap at Adobe by enforcing reuse of Photoshop imaging tech to deliver RAW, non-destructive mobile editing with full Creative Cloud compatibility. In startup environments, has repeatedly scaled and professionalized engineering orgs (career ladders, evaluation systems, delivery process) and led high-stakes cloud/platform transformations—migrating off Heroku and consolidating fragmented AWS/GCP usage into a single GCP platform—improving reliability, deploy speed, and cutting cloud costs by 20%+.”

“Senior engineering/technology leader with experience driving board-backed, multi-year platform transformations in retail commerce (American Eagle) and scaling ML/product delivery platforms at Amazon Alexa. Known for data-driven build-vs-buy decisions, org scaling (6→80), and measurable outcomes including 98.5%→99.9% uptime, 234 experiments generating ~$200M revenue lift, and increasing delivery velocity from 1.5 to 30 features/week while transitioning to DNN/large-model architectures.”

Danny Anop

Screened

Senior Full-Stack Engineer specializing in SaaS, e-commerce, and frontend platforms

Bellevue, WA10y exp

“Frontend-leaning full-stack engineer who has built a multi-tenant AI-powered widget and admin dashboard platform used across 40+ merchant websites. Strong in TypeScript/Next.js/GraphQL systems design, reusable platform primitives, and cross-layer debugging, with a clear track record of shipping scalable product experiences under ambiguity.”

Christopher Sipola

Screened

Senior Machine Learning Engineer specializing in LLMs and recommendation systems

New York, NY11y exp

“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”

SHIVA SANDY

Screened

Executive CTO/CIO specializing in FinTech, cloud transformation, and cybersecurity

Walnut Creek, CA27y exp

“Banking/credit-union technology executive (CTO/CTIO) who has led modernization and product delivery through major change, including a post-merger roadmap that helped grow a credit union from $600M to $1.5B in 3 years. Hands-on leader who drove cloud microservices/API architecture, introduced CI/CD, and accelerated releases from quarterly to weekly while scaling teams and operating effectively with distributed/remote orgs.”

“Hands-on engineering leader with early-stage startup experience who balances coding with executive alignment and delegation. Has delivered offline-capable SaaS mobile demos under tight timelines, built monetization-ready mobile architectures (dynamic cloud assets + in-app purchases), and increased product velocity via component-based game architecture plus stakeholder self-serve tooling.”

“Senior Microsoft engineering leader who has led globally distributed orgs of up to 250 engineers while staying deeply technical in large-scale platform architecture, reliability, and AI systems. Notable work includes modernizing Teams infrastructure serving massive global scale and incubating an internal AI pair-programmer/knowledge-graph platform over 650M lines of code, plus helping launch foundational Copilot search and retrieval APIs.”

“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”