.png){kind=logo}
Vetted Tableau Professionals
Pre-screened and vetted.
Junior Data Analyst specializing in business intelligence and Salesforce data systems
Mid-level Full-Stack Software Engineer specializing in cloud microservices and data/ML
Entry-Level UX Designer and Computer Science Graduate specializing in data-driven product design
Mid-level R&D Engineer specializing in applied AI, experimentation, and cloud-integrated prototypes
Mid-level Data Analyst specializing in marketing and product analytics
Intern Full-Stack Engineer specializing in web applications and FinTech tools
Mid-level IT Support & Networking Technician with cybersecurity and customer service experience
Junior Full-Stack Software Engineer specializing in web apps, cloud, and ML inference
Mid-level Sales Development Representative specializing in B2B SaaS and FinTech
Junior Full-Stack Software Engineer specializing in cloud-native microservices and data pipelines
Mid-level Data Analyst specializing in BI dashboards, cloud data platforms, and ML analytics
Mid-level Sales Development Representative specializing in outbound prospecting and pipeline generation
Mid-level Machine Learning Engineer specializing in Generative AI and healthcare NLP
Entry-Level Software Engineer specializing in backend services and applied ML
Mid-level Data Analyst specializing in aerospace materials testing and QA reporting
Junior Data Scientist/Data Analyst specializing in machine learning and business intelligence
Senior Creator Marketing & Social Media Manager specializing in entertainment and music
Mid-level QA Tester specializing in game testing across mobile, PC, console, and VR
Intern-level marketing analyst specializing in digital marketing and lead generation
Manas Kumar Sherla
Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI
“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”
“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”