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Vetted Power BI Professionals
Pre-screened and vetted.
Mid-level Full-Stack Software Engineer specializing in cloud-native data platforms
Junior Finance & Strategy Analyst specializing in business analytics and financial modeling
Mid-Level Full-Stack Software Engineer specializing in Java/Spring Boot microservices
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
Mid-level Software Engineer specializing in cloud-native microservices and ML-driven automation
Intern-level Software Engineer specializing in Machine Learning and Full-Stack Web Development
Mid-level Data Engineer specializing in cloud-native ETL and data warehousing
Junior Software Engineer specializing in LLMs, RAG, and Knowledge Graphs
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Senior RPA & Automation Architect specializing in Agentic AI and enterprise hyperautomation
Junior Marketing Analyst specializing in growth and performance analytics
Senior Product/UX Designer specializing in enterprise UX, AI, and modern interfaces
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Mid-level Supply Chain & Procurement Specialist in strategic sourcing and P2P (SAP/Oracle)
Intern Machine Learning Engineer specializing in LLMs, retrieval, and vision-language models
Senior Supply Chain Analyst & Demand Planner specializing in S&OP, forecasting, and inventory optimization
Junior Full-Stack/Cloud Engineer specializing in AI and data-driven applications
Mid-level AI/ML Product & Solutions Specialist specializing in GenAI and MLOps
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Adithya Rajendra
Junior Data Engineer specializing in Azure data platforms and GenAI analytics
“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”