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Vetted ETL Professionals
Pre-screened and vetted.
Junior Data Analyst specializing in AI-driven analytics and business intelligence
Junior Data Analyst specializing in business intelligence and Salesforce data systems
Mid-level Full-Stack Software Engineer specializing in cloud microservices and data/ML
Senior Machine Learning Engineer specializing in NLP and production ML systems
Mid-level R&D Engineer specializing in applied AI, experimentation, and cloud-integrated prototypes
Junior .NET Developer specializing in Azure and web application development
Mid-level Python Backend Engineer specializing in microservices and AWS
Junior Software Engineer specializing in AI/ML, data pipelines, and real-time dashboards
Junior Full-Stack Software Engineer specializing in cloud-native microservices and data pipelines
Senior AI Engineer specializing in LLMs, agentic systems, and ML infrastructure
Mid-level Machine Learning Engineer specializing in Generative AI and healthcare NLP
Mid-level QA Engineer specializing in manual testing, UAT, and API/SQL validation
Mid-level Data Analyst specializing in aerospace materials testing and QA reporting
Manas Kumar Sherla
Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI
“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”
Mid-Level Full-Stack Developer specializing in Python/FastAPI and React
Junior Full-Stack Data Engineer specializing in data pipelines and analytics
Anjali Chepuri
“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”