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Vetted Azure Data Factory Professionals
Pre-screened and vetted.
Senior Full-Stack Software Engineer specializing in cloud-native microservices and AI/ML
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time AI systems
Mid-level Data Engineer specializing in cloud ETL, streaming, and data warehousing
Mid-Level Full-Stack Developer specializing in cloud-native microservices and GenAI
Mid-level AI/ML Engineer specializing in NLP and MLOps for regulated industries
Mid-level Data Engineer specializing in cloud ETL, big data, and analytics
Senior Data Engineer specializing in real-time pipelines, cloud data platforms, and healthcare analytics
Senior Data Analyst & Data Scientist specializing in healthcare, epidemiology, and predictive modeling
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
Senior Data Engineer specializing in AWS cloud data platforms and streaming analytics
Senior Cloud Engineer specializing in AWS & Azure infrastructure, security, and automation
Senior Full-Stack Software Engineer specializing in React, React Native, and Spring Boot
Mid-level Data Engineer specializing in lakehouse architectures and cloud ELT
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
GOWRI SHANKAR ANANTHULA
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”
Sharanya Rao
Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare
“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”