.png){kind=logo}
Vetted Random Forest Professionals
Pre-screened and vetted.
Intern-level Full-Stack Software Engineer specializing in AI and web applications
Intern Data Scientist specializing in LLM agents, RAG, and real-time ML pipelines
Mid-level Data Scientist specializing in GenAI, MLOps, and computer vision for robotics
Entry-Level Data Scientist specializing in machine learning, NLP, and cloud analytics
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Intern Machine Learning & Computer Vision Engineer specializing in 3D reconstruction
Intern Full-Stack & AI Engineer specializing in ML-driven mobile and data platforms
Mid-level Full-Stack Engineer specializing in backend systems and AI integration
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Junior Software Engineer specializing in data analytics and machine learning
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Entry-Level Full-Stack Engineer specializing in backend APIs and cloud architectures
Junior Full-Stack Developer specializing in Django/React and cloud-native APIs
Jeevan Kumar
Mid-level Full-Stack AI Engineer specializing in LLM systems and RAG
“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”
Junior Software/AI Engineer specializing in LLM agents and RAG systems
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.”
Rasheed Samady
Entry-level AI Engineer specializing in automation and ML platforms
“Built a production Python lead intelligence pipeline that combined external APIs, website crawling, and automated opportunity brief generation, with strong emphasis on reliability, observability, and recovery. Also has hands-on Playwright experience hardening flaky, dynamic web automations and reducing intermittent failures to under 5% through logging, screenshots, session management, and retry strategies.”
Aditya Reddy Gunda
Intern AI/ML & Data Engineer specializing in deep learning, NLP, and cloud data pipelines
“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”
Sadir Ahmed Zidan
Junior AI/ML Engineer specializing in LLMs, RAG, and computer vision
“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”