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Vetted Predictive Analytics Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in cloud-native SaaS and AI-integrated platforms
Mid-level Full-Stack AI Engineer specializing in web and generative AI solutions
Senior Performance & Growth Marketing Leader specializing in paid media, automation, and analytics
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Muhammad Murtaza Murtaza
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics
“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”
Zara Khan
Mid-level Unity Game Developer specializing in mobile and multiplayer games
“Unity developer with roughly 5 years of experience who speaks fluently about shipping performance-sensitive gameplay systems, multiplayer features, and AI/LLM-driven NPC dialogue. Particularly notable for combining technical implementation with analytics, live ops monitoring, and cross-platform constraints across Meta Quest VR and mobile.”
Sirisha Gadde
Intern Data Analyst specializing in analytics and machine learning
“FAU-based analytics candidate with hands-on academic project experience across SQL data preparation, Python/NLP sentiment analysis, and predictive modeling. They stand out for turning messy datasets into clean reporting tables, building reproducible analysis workflows, and translating findings into practical recommendations around operations, credit risk, and marketing ROI.”
KARISHMA PAPPU
Junior AI/ML Engineer specializing in machine learning and data pipelines
“Built and productionized an LLM-based system that summarizes large volumes of unstructured content (customer feedback/internal docs) to reduce manual analysis and surface decision-ready insights. Brings strong reliability practices—prompt/schema constraints, validation checks, orchestration with Airflow/Databricks, and rigorous component + end-to-end testing—plus experience partnering closely with business stakeholders to drive adoption.”
Abdirazak Farah
Entry-Level Full-Stack/IoT Engineer specializing in AI-powered applications
“New-grad software engineer who built a real-world smart-city traffic camera/operations dashboard pitched to Peachtree Corners, GA, integrating Bosch cameras with an MQTT/Node-RED/InfluxDB pipeline and Grafana visualizations. Implemented rule-based alerts (including SMS dispatch for janitorial cleaning) and improved production reliability with queuing, redundancy, monitoring, and load testing, reaching ~99% message delivery. Actively seeking onsite, customer-facing roles with travel.”
Junior Salesforce Administrator specializing in automation, integrations, and analytics
Mid-level Account Executive specializing in enterprise B2B sales and revenue growth
Junior Data Analyst specializing in AI-driven analytics and business intelligence
Mid-level Python Developer specializing in cloud-native APIs and microservices
Mid-level Full-Stack AI Engineer specializing in RAG systems and intelligent automation
Mid-level Data Analyst specializing in BI dashboards, cloud data platforms, and ML analytics
Junior Sports Administration professional specializing in athletic program operations and athlete development
Minh Huynh
Junior AI/ML Engineer specializing in LLM systems and personalization
“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”