Vetted ETL Pipelines Professionals

“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”

“Backend-leaning full-stack engineer in lending/financial services (Kotak Mahindra Bank Autos360; currently at Ally Financial) working on Spring Boot microservices with React dashboards. Has built reliability improvements for credit-bureau integrations (Experian) and created an internal monitoring/reporting platform that aggregates metrics/logs/ETL across services, cutting troubleshooting by ~40%.”

“Backend engineer/technical lead with experience building and operating real-time blockchain analytics systems at Merkle Science. Owned high-traffic Django/DRF services and Kafka streaming pipelines processing millions of events daily, with deep focus on performance (N+1 fixes, indexing, caching) and reliability (DLQs, retries, monitoring). Also led containerization and Kubernetes/GitOps-style CI/CD on Google Cloud, including a migration off Google App Engine to reduce cost and improve scalability.”

“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.”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“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.”

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“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.”

Erik Moyer

Screened

Director-level Data Science & Analytics Leader specializing in cloud data platforms and AI/ML

Dallas, TX13y exp

“Candidate states they are very familiar with the venture capital/studio/accelerator landscape and expresses strong willingness to pursue entrepreneurship "at all costs," but did not provide details on a current startup, business plan, fundraising, or prior accelerator/VC involvement during the interview.”

“Software engineer experienced owning internal, customer-facing dashboards and internal ops tools end-to-end, emphasizing fast iteration without sacrificing stability (CI/CD, automated tests, feature flags, monitoring). Built a TypeScript/React role-based dashboard backed by Java Spring Boot and has hands-on microservices experience with RabbitMQ, including production hardening with retries, dead-letter queues, logging, and health checks.”

“Engineering/technology leader (CTO-style) with experience scaling orgs and running distributed teams across four continents for over a decade. Led a high-stakes modernization of a securities trading platform at Wedbush—migrating from monolith to microservices on AWS with zero-downtime constraints—driving 45% execution performance improvement and enabling 25% market share growth. Emphasizes business-aligned roadmaps, build-vs-buy rigor, and scalable engineering practices/culture.”

Muaaz Syed

Screened

Mid-level AI/ML Engineer specializing in NLP and conversational AI

Richardson, TX4y exp

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”