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Vetted Azure Data Factory Professionals
Pre-screened and vetted.
Mid-level Machine Learning & GenAI Engineer specializing in RAG and multimodal AI systems
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Mid-level AI & Data Engineer specializing in cloud ML, RAG systems, and ETL automation
Mid-level Full-Stack .NET Engineer specializing in cloud, APIs, and data analytics
Mid-Level Software Engineer specializing in backend, cloud, and data pipelines
Junior Software Developer specializing in full-stack and data engineering
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Mid-Level Full-Stack .NET Engineer specializing in cloud, APIs, and data analytics
Mid-level Software QA Engineer specializing in web, API, and test automation
Abhijeet Gajjar
Mid-level Mobile Software Engineer specializing in iOS, React Native, and AI-enabled backends
“Backend engineer who built and scaled a FastAPI-based backend for an AI-driven maintenance system automating vendor sourcing/bidding/communication. Emphasizes async, message-driven architecture with strong observability and state-machine-driven workflows, plus robust webhook/idempotency patterns to prevent duplicate/out-of-order events from causing bad bids or state changes.”
Junior Machine Learning Engineer specializing in healthcare and IT analytics
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Mid-level Software Engineer specializing in Generative AI and cloud-native microservices
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Mid-Level Full-Stack Software Engineer specializing in cloud microservices and Voice AI
Mahaboob Johny
Mid-level Data Analyst specializing in SQL/Python analytics, ETL pipelines, and BI dashboards
“Data/AI practitioner who built a production LLM-driven healthcare claims analytics and dashboarding system to reduce avoidable ER visits—processing 1.4M+ claims, flagging 19% as non-emergent, and projecting ~$2.8M in annual savings. Demonstrates strong real-world LLM reliability and performance engineering (grounding, numeric validation, caching, materialized views, quantization) plus orchestration experience with Airflow and Azure Data Factory.”