Vetted BigQuery Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“Built and owned an end-to-end agentic RAG chatbot platform for Baptist Health that helped clinicians access policy and clinical documents faster, reducing manual lookup by 80% and delivering about $2M in annual savings. Brings strong healthcare GenAI production experience, including HIPAA-aligned governance, PHI redaction, observability, evaluation, and scalable Python/Kubernetes deployment practices.”

“Analytics professional with experience across healthcare and fintech, including building SQL/Python data pipelines at Optum and owning a fraud detection initiative at Razorpay. Stands out for combining messy-data cleanup, reproducible analytics workflows, and stakeholder-driven metric design, with a reported 25% improvement in fraud detection while keeping false positives under control.”

“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”

Ayush Gupta

Screened

Mid-level AI Engineer specializing in Agentic AI and Generative AI

6y exp

“Built and deployed a live LLM-powered platform that takes a LinkedIn job URL + resume and generates job-specific resumes and personalized outreach at scale, with production-grade logging/monitoring/retries on Vercel + Railway. Experienced with agent orchestration (AWS Bedrock/Strands, LangGraph, CrewAI) and rigorous AI workflow testing, plus stakeholder-facing prototypes like data lineage/metadata and NL-to-SQL + dashboard generation.”

Priyanshu Maurya

Screened

Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics

New York, NY3y exp

“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”

“Backend/ML systems engineer with experience at SDSC, UCSD, and Media.net, building production semantic dataset/model discovery using embeddings + Solr KNN and LLM-based intent/reranking at 5M+ dataset scale. Emphasizes offline/online separation for predictable serving, has delivered measurable gains (23% retrieval accuracy, 38% latency reduction) and helped secure a $3M+ NSF grant.”

“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”

“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”