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Vetted Apache Hive Professionals
Pre-screened and vetted.
Mid-level AI/ML Developer specializing in healthcare and e-commerce ML systems
Mid-level AI/ML Engineer specializing in Generative AI and NLP
Mid-level Data Engineer specializing in cloud ETL, streaming, and data warehousing
Director-level Cloud & DevOps Leader specializing in multi-cloud modernization and GenAI platforms
Mid-level Data Scientist / AI/ML Engineer specializing in secure cloud ML and GenAI
Mid-level AI Engineer & Data Scientist specializing in Generative AI, NLP, and Cloud ML
Junior Data Scientist specializing in risk modeling, NLP, and predictive analytics
Mid-level Data Engineer specializing in cloud ETL, big data, and analytics
Senior Data Scientist specializing in machine learning and cloud analytics
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level Full-Stack Engineer specializing in FinTech and real-time distributed systems
Mid-level AI/ML Engineer specializing in MLOps, NLP/CV, and fraud detection
Senior Data Engineer specializing in AWS cloud data platforms and streaming analytics
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior AI/ML Engineer specializing in LLM, NLP, and production ML systems
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Varshitha K
Mid-level Data Engineer specializing in cloud data platforms and lakehouse architectures
“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”
Chandan Chalumuri
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”
Fernando Mosqueda
Senior AI/ML Engineer specializing in healthcare AI and MLOps
“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”