Vetted ETL Pipelines Professionals

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

“Backend/DevOps-leaning engineer who has owned a Python serverless platform on AWS (Lambda, DynamoDB, Step Functions), including complex multi-step business workflows with transaction-based consistency and robust failure handling. Also supported an on-prem SQL to Azure Data Lake migration by building and monitoring Python + Azure Data Factory ETL pipelines, and led GitOps-style CI/CD automation with GitHub Actions (tests, security scans, automated deployments).”

Riya Stanly

Screened

Intern Data Scientist specializing in machine learning, NLP, and LLM fine-tuning

2y exp

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”

HarshaVardhanRao Vemuganti

Screened

Junior AI/ML Engineer specializing in Generative and Agentic AI

Lafayette, Louisiana4y exp

“Built and deployed a production-grade LLM agent for credit management and accounts receivable automation, integrating ERP/MySQL data via a RAG pipeline and exposing services through FastAPI with Pydantic-validated outputs on AWS Bedrock. Emphasizes reliability and compliance for financial operations using schema validation and human-in-the-loop review, reporting ~32% reduction in manual work and ~41% improvement in response time/reliability.”

“AI/ML engineer with 9+ years of experience building production recommendation and LLM systems end-to-end, from experimentation through deployment, monitoring, and retraining. Stands out for combining strong MLOps discipline with practical GenAI/RAG implementation, including measurable impact such as ~25% higher engagement on an e-commerce recommender and nearly 30% faster knowledge retrieval from internal documents.”

Joseph Heaney

Screened

Mid-level Data Engineer specializing in ETL pipelines, BI, and cloud data platforms

Brooklyn, NY3y exp

“Data- and backend-leaning full-stack candidate with hands-on experience building Python ETL pipelines, complex SQL reporting, and Power BI infrastructure at Eastman. They improved internal operations by consolidating a multi-sheet task workflow into a simpler automated system and have repeatedly delivered reporting solutions by iterating directly with business stakeholders under ambiguous requirements.”

“Full-stack product engineer with hands-on ownership of both real-time product infrastructure and production AI systems. They shipped a notification platform that materially improved engagement and repeat purchases, and also built key layers of an AI support agent with strong reliability controls and user-centered UX improvements that boosted adoption.”

“ML/AI engineer with hands-on experience building healthcare and fraud-detection systems from experimentation through deployment, monitoring, and retraining. Stands out for combining real-time IoT pipelines, cloud-native MLOps, and GenAI/RAG in regulated healthcare settings, with reported impact including reduced emergency response times and a 25% reduction in manual diagnosis time.”