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Vetted ETL Pipelines Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Engineer specializing in cloud, microservices, and DevOps
Junior Software Engineer specializing in AI agents and distributed data systems
Junior Business Analyst specializing in data analytics and customer behavior insights
Mid-level Data Engineer and AI Engineer specializing in LLMs and data platforms
Mid-level AI Software Engineer specializing in LLMs and healthcare AI
Mid-level Full-Stack Engineer specializing in AI, agentic systems, and LLM infrastructure
Senior Full-Stack Engineer specializing in React, AWS, and API-driven platforms
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level Backend Software Engineer specializing in cloud microservices and AI agent systems
Mid-Level Machine Learning Engineer specializing in LLMs and RAG systems
Mid-level Full-Stack AI Engineer specializing in agentic RAG and LLM fine-tuning
Mid-level Machine Learning Engineer specializing in distributed AI systems
Mid AI/ML Engineer specializing in LLMs, MLOps, and FinTech analytics
Senior Full-Stack Engineer specializing in e-commerce and FinTech
Senior Full-Stack Engineer specializing in React, Python, and AI-driven SaaS
Mid-level GenAI/ML Engineer specializing in enterprise LLM and RAG systems
Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
“Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.”
Ruthvika Mamidyala
Mid-level Data Scientist specializing in GenAI, RAG, and predictive modeling
“Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.”
Helly Shah
Junior Data Analyst specializing in business analytics and machine learning
“Analytics-focused candidate with hands-on project experience in SQL data preparation and Python-based churn modeling. They demonstrated a practical approach to turning messy multi-source data into reporting tables, validating data quality rigorously, and translating churn insights into targeted retention strategies.”