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Vetted Clustering Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM, RAG, and semantic search systems
Junior Machine Learning Engineer specializing in fraud detection, KYC, and LLM applications
Junior Game Designer specializing in gameplay systems, combat, and AI (Unity/Unreal)
Mid-Level Full-Stack Software Engineer specializing in microservices and Angular/.NET
Mid-level Data Scientist specializing in ML, NLP and forecasting
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
Mid-level Data Engineer specializing in cloud data pipelines for Healthcare and FinTech
Mid-level Data Engineer specializing in AI, analytics, and cloud data platforms
Mid-level Software Engineer specializing in full-stack data systems and cloud automation
Junior Full-Stack Software Engineer specializing in web, mobile, and AI-enabled collaboration tools
Mid-Level Full-Stack Software Engineer specializing in SaaS logistics and cloud-native systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI/ML Engineer specializing in cloud AI, MLOps, and NLP
Mid-level Data Analyst specializing in marketing analytics and machine learning
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”