Vetted R Professionals

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”

“Omnichannel paid media specialist with hands-on ownership across search, social, programmatic, CTV, TV, audio, and print, spanning both digital and traditional media. Has managed up to $750K/month in spend and cites a recent win driving a 51% lift in conversions while cutting CPA by 119% for Bookkeepers.com. Particularly strong in self-service buying, optimization, and client-facing strategic pivots across diverse industries.”

“Architecture leader from Disney who managed system, AI, and data architects while staying hands-on in solution design. Has experience building LLM-based video advertising products, designing Kafka-based real-time data architectures, and using MVP/POC approaches to align product and executive stakeholders.”

“MSR robotics candidate who led a 4-person project building a ROS2 MoveIt wrapper for a Franka Emika arm and integrating a RealSense-based vision pipeline for color-based object tracking/sorting. Also building a quadruped with ROS on Raspberry Pi, bridging ROS commands through a motor driver to TTL-controlled motors, and expanding from Python ROS development into C++ for navigation/LiDAR/SLAM work on TurtleBot3.”

“Built and shipped open-source LLM/RAG systems, including a generative AI assistant grounded on ~30,000 scraped university web pages, improving response accuracy ~30% by moving from TF-IDF-only retrieval to a hybrid sentence-transformer approach with fallback controls. Also partnered with non-technical leadership at Securi.ai to deliver real-time predictive analytics dashboards (Elasticsearch + Jira/ServiceNow) that reduced project overhead by 18%.”

“Built a production-style PyTorch LSTM system that generates structured piano compositions from 1200+ MIDI files, then significantly improved long-range musical coherence by implementing Bahdanau attention based on research literature. Also has internship experience using Docker Compose for containerized backend workloads and has independently used Ray to scale ML experiments across multiple GPUs, including dealing with GPU scheduling/memory oversubscription issues.”

“Enterprise Architecture leader with insurance domain experience (Farmers Insurance) who drove a multi-phase roadmap to modernize a siloed CRM landscape—migrating from legacy Siebel to Salesforce Financial Services Cloud with Customer 360, MDM, and omnichannel capabilities. Also led a high-impact architecture decision to implement offline billing to reduce customer-facing downtime, including complex SAP/on-prem-to-cloud integration and transaction sync.”

“Software engineer with experience at Striim and Amazon who ships end-to-end production systems across UI, backend, ML, and operations. Built a real-time PII detection capability for a streaming data platform by integrating Python ML inference into a Java monolith via gRPC sidecars, achieving ~3M events/hour throughput and ~93% accuracy, and helped drive enterprise adoption (Fiserv, CVS). Also modernized internal Amazon tooling for multi-region scale with modularization and fully automated deployments.”

David Zeibert

Screened

Junior Development Analytics Analyst specializing in QSR growth and automation

Miami, FL3y exp

“Data-driven economy/incentives designer with experience across QSR brands (Popeyes and Burger King), spanning franchise development incentive systems and in-app game economies. Built live scorecards (Snowflake/SQL/Tableau) and regression-based sales forecasting models on thousands of restaurant records, and used app telemetry to tune progression loops and improve retention while aligning ops and business KPIs.”

Bhanu Chander

Screened

Senior Data Engineer specializing in cloud data platforms and real-time pipelines

New York, NY6y exp

“Data engineer focused on reliability and observability, building end-to-end pipelines processing millions of records/day from sources like S3 and Kafka. Has hands-on experience with Airflow-based data quality automation, PySpark/Databricks transformations, and shipping versioned Python REST APIs deployed via Docker/Kubernetes with CI/CD (Jenkins) and monitoring (CloudWatch/Azure Logs).”

“Data engineer with experience at Expedia building SQL Server and Azure Data Factory pipelines for business reporting and analytics. Stands out for pragmatic end-to-end pipeline ownership in ambiguous environments, with a strong emphasis on data quality, rerunnability, query performance, and making downstream datasets reliable for other teams.”

“GenAI/LLM engineer who architected and deployed a production RAG “research assistant” for JPMorgan Chase’s regulatory compliance team, focused on safety-critical behavior (mandatory citations, refusal when evidence is missing). Deep hands-on experience with LlamaIndex, Pinecone, Hugging Face embeddings, LangGraph agent workflows, and metric-driven evaluation (golden sets, TruLens), including a reported 28% relevancy lift via cross-encoder re-ranking.”

“Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.”

“Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.”

“LLM/MLOps engineer who has shipped production systems for complaint intelligence and contact-center NLU, including LoRA/RLHF-tuned LLaMA models deployed on GKE with vLLM and Vertex AI batch pipelines to BigQuery. Demonstrates strong practical focus on hallucination control, data imbalance mitigation, and production monitoring (Langfuse) with regression testing and canary rollouts, plus experience orchestrating complex workflows with AWS Step Functions.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“ML/LLM engineer who has shipped a production transformer-based document understanding system on AWS, owning the full pipeline from domain fine-tuning to Dockerized CI/CD deployment. Demonstrates strong production rigor—latency optimization (distillation/quantization, async batching, autoscaling), orchestration with Airflow/Step Functions/Azure Data Factory, and monitoring/drift detection—plus experience translating ops stakeholder needs into adopted AI automation via dashboards.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

“Enterprise SaaS implementation specialist (Sprinklr) who owns complex rollouts end-to-end—configuration, data migration, UAT, global enablement—and drove a 10/10 Customer Happiness Index plus renewal/expansion through strong adoption and analytics automation. Also has product experience from a TikTok PM internship leading an accessibility initiative (Camera AI for screen reader creators) coordinating engineering, design, legal, and QA with WCAG/ADA/EAA compliance considerations.”

Vivek Reddy

Screened

Mid-level Data Scientist/Data Engineer specializing in ML pipelines, insurance and healthcare analytics

Los Angeles, CA7y exp

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

“Analytics-focused candidate with hands-on experience building SQL reporting tables from messy transactional and master data, plus Python workflows that automate monthly analysis and data checks. They appear strongest in KPI/reporting ownership, metric standardization, and stakeholder alignment, with examples of improving reporting consistency, surfacing issues earlier, and reducing manual reconciliation effort.”

Hargun Kaur Kohli

Screened

Intern Business Analyst specializing in analytics and marketing insights

California, USA1y exp

“Graduate-school capstone and project work centered on analytics, including healthcare shipment/pharmacy data, customer recommendation modeling, and a gaming uplift modeling project. Stands out for framing analytics around business impact—using uplift, repeat purchase, and profit-oriented metrics rather than just accuracy.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”

“Operations- and systems-minded candidate with experience improving hospital billing workflows and automating a campus package pickup/drop-off system that streamlined operations for 400 students and 20 student workers. While they do not have direct outbound prospecting experience, they articulated a thoughtful, research-driven framework for building an SDR pipeline engine from scratch.”