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Vetted Apache Airflow Professionals
Pre-screened and vetted.
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Senior Data Analyst specializing in healthcare, insurance, and financial analytics
Senior Data Analyst specializing in BI, data engineering, and predictive analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-level Software Engineer specializing in backend APIs and data pipelines
Mid-level Data Scientist specializing in ML, NLP, and analytics for FinTech
Mid-Level Software Development Engineer specializing in cloud platforms, IAM, and secure GenAI
Mid-level Data Engineer specializing in cloud ETL, streaming, and data warehousing
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
Mid-level Data Engineer specializing in cloud data pipelines and full-stack analytics
Mid-level Data Engineer specializing in AWS, Snowflake, Databricks, and PySpark
Senior Full-Stack Software Engineer specializing in cloud-native FinTech and data pipelines
Senior Full-Stack Python Developer specializing in microservices, data engineering, and cloud
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”
Engineering Leader & Principal Software Engineer specializing in cloud-native SaaS
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines