Vetted Distributed Tracing Professionals

Pre-screened and vetted.

SK

Mid-Level Software Engineer specializing in desktop applications and legacy modernization

Portland, Oregon4y exp
Great Valley TechnologiesKremenchuk Mykhailo Ostrohradskyi National University

Full-stack engineer working on a medical practice SaaS, who owned an end-to-end charge entry module handling patient charges/payment workflows and complex client-side data transformations. Also built a fintech rating platform with real-time WebSocket streaming and TradingView-based charting, and has 10 years of university experience mentoring student "startup" capstone projects under tight deadlines.

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MM

Junior Full-Stack & Machine Learning Engineer specializing in observability tools

New York, United States2y exp
SignalStackFES Institute
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EU

Mid-level Gameplay Programmer specializing in maintainable gameplay systems

Edirne, Turkey3y exp
FreelanceTrakya University
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MR

Intern Full-Stack Software Engineer specializing in AI-powered RAG systems

Lebanon0y exp
CodVedaLebanese University

Built FlowPilot, an AI-powered product that generates complete importable n8n workflows from natural-language prompts using a RAG pipeline (Qdrant + LangChain) and a multi-stage agent with a scoring/repair 'Judge' loop for intent alignment. Experienced in backend architecture across Laravel/Node microservices and production AI/RAG systems, plus performance debugging from async job offloading to database index tuning after ORM migrations.

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AA

Entry Machine Learning Engineer specializing in quantitative finance and DeFi

Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.

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Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.

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