Vetted Random Forest Professionals

“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.”

Muhammad Murtaza Murtaza

Screened

Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics

Islamabad, Pakistan5y exp

“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.”

“Built production-style backend and AI systems across internship and project work, including a real-time sports platform backend and a Smart Email Assistant using GPT-4. Stands out for combining classic backend performance engineering with practical LLM workflow design, including measurable latency improvements, high uptime, and debugging of non-deterministic model behavior.”

“Full-stack engineer with strong TypeScript/Firebase and MongoDB depth who has built research and student-scale systems end-to-end, including GCP data pipelines and Dockerized microservices. Particularly interesting for stepping beyond a frontend remit to build NLP models with NLTK that are actively used in an academic research project's Phase 2, showing initiative and real downstream impact.”

Rasheed Samady

Screened

Entry-level AI Engineer specializing in automation and ML platforms

San Francisco, CA0y exp

“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.”

“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.”

“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.”

“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”