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Vetted Time Series Forecasting Professionals
Pre-screened and vetted.
Mid-level AI/ML Data Engineer specializing in MLOps and Generative AI
Junior Data Scientist specializing in LLMs, RAG, and agentic AI systems
Mid-level Machine Learning Engineer specializing in multimodal GenAI and RAG systems
Mid-level Data Scientist / AI/ML Engineer specializing in MLOps, geospatial analytics, and GenAI
Mid-level AI & Data Engineer specializing in cloud ML, RAG systems, and ETL automation
Mid-level AI/ML Engineer specializing in generative AI, NLP, and MLOps
Mid-level Data Scientist specializing in deep learning, NLP, and time-series forecasting
Mid-level Data Scientist specializing in NLP, Generative AI, and ML pipelines
Mid-level Data Analyst specializing in BI, analytics, and data engineering
Senior AI/ML Engineer specializing in Generative AI, LLMs, and data platforms
Mid-level Full-Stack Engineer specializing in AI-driven web applications
Mid-level AI Product Engineer and Data Analyst specializing in LLM automation
Junior Data & BI Analyst specializing in analytics engineering, NLP, and financial risk analytics
Senior Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Samuel Braude
Junior Computer Science student specializing in robotics, ML, and quantum computing research
“Hands-on engineer who has taken an LSTM Bitcoin forecasting model from notebook to a production-grade, monitored API (Docker/Gunicorn/Nginx, Prometheus/Grafana, blue-green rollback) delivering 99.9% availability and ~110–120ms p95 latency. Also built an RFID self-checkout prototype spanning Raspberry Pi + firmware + networking, using deep instrumentation to eliminate double-charges/timeouts (<0.1%) and reduce checkout time ~20% through idempotency, debounce logic, and hardware fixes.”
Balakrishna Mylapilli
Mid-level AIML Engineer specializing in production ML and MLOps
“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”
MohammadHossein Karimian
Mid-level Front-End Developer specializing in React and TypeScript
“Frontend engineer who has led end-to-end builds of complex React + TypeScript workflow editors (multi-step scenario builder with nodes/connections/conditions) with strong quality practices (CI/CD, unit tests, schema validation, logging, feature flags). Also delivered an AR flower-placement feature during an internship at Ecomspiders, rebuilding the experience with Three.js, live camera preview, and surface placement tested across devices and lighting conditions.”
Prasanth Kumar Korada
Mid-level QA Automation Engineer specializing in Playwright and cross-browser E2E testing
“QA automation engineer with strong end-to-end ownership of UI automation for financial transaction workflows, using Selenium/Playwright/Cypress and CI/CD gating. Improved suite robustness via UI-API validations, negative testing, and flake reduction (intercepts + data-testid), catching critical backend calculation issues before production and cutting regression runtime by 40%.”