Vetted R Professionals

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“字节跳动实习期间将内部AI重量预测模型从“可用但难上线”的单点能力，改造成可商业化复用的通用API：统一多地区接口与评估口径，设计分层兜底与置信度分级，先灰度上线SEA/JP并推动US/EU落地，结合线上结果进行模型微调。具备LLM/RAG/Agent系统的实战排障方法论，以及面向开发者与售前场景的技术演示与跨团队推进能力。”

“AI engineer who built a production RAG-based internal analyst tool at BlackRock, fine-tuning an LLM on proprietary financial data and adding four layers of guardrails (input/retrieval/generation/output) to improve grounding and reduce hallucinations. Implemented a LangChain-based multi-agent orchestration (7 major agents) deployed on AWS ECS, with reliability measured via internal human evaluation, LLM-as-judge, and RLHF/drift monitoring.”

“Sourcing/procurement professional with Accenture experience supporting telecom infrastructure expansion, owning RFQs through delivery and managing 12+ vendors. Known for strong supplier performance management (scorecards, KPIs, corrective actions) delivering ~97% on-time material delivery while cutting expedited freight 13% and avoiding ~AUD 500K in delay costs; also brings supplier onboarding and capability/capacity assessment experience from Nestlé and Accenture.”

“Robotics software engineer focused on real-time estimation/control and motion replanning, currently integrating a factor-graph-based estimation/control stack with sampling-based replanning in a ROS environment validated on both MuSHR hardware and MuJoCo simulation. Strong in distributed-system debugging (rosbags/logging, controlled test scenarios) and ROS performance patterns (nodelets, TF/TF2), with prior multi-robot experience from SSL RoboCup using custom UDP protocols.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”

“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”

“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”

“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”

“ServiceNow engineer who built and launched a production LLM-powered ticket resolution/knowledge assistant using RAG (LangChain + Hugging Face embeddings + vector search) integrated into internal support dashboards via REST APIs. Optimized the system from ~6–8s to ~2–3s latency while improving usability with concise, cited answers and guardrails (grounding + similarity thresholds), delivering ~30–35% reduction in manual ticket investigation effort.”

“Backend/founding-engineer-style builder who designed and evolved a near-real-time customer churn prediction platform (FastAPI + AWS SageMaker/Lambda + Redis + MLflow) to enable real-time retention actions, reporting ~18% churn reduction. Demonstrates strong production engineering in secure API design, incremental migrations with data integrity safeguards, and robustness improvements in async pipelines (idempotency, DLQs, retry visibility).”

“Growth marketer from earthnutre who led a zero-to-one conversion and traffic-quality improvement project combining SEO, landing page/CRO, and paid-media measurement. Used SEMrush-driven high-intent keyword restructuring plus phased page optimizations with control comparisons, then tied results to paid performance—reporting ~15% lower CPA and ~30% MoM ROAS lift. Also runs a modular, data-driven creative iteration process across Meta/TikTok/YouTube and provides performance-based guidance to creators/editors.”

“GenAI/ML engineer from Lucid Motors who built and productionized an LLM-powered RAG diagnostic assistant for manufacturing and maintenance teams, deployed on AWS with Docker/Kubernetes and MLflow. Demonstrates end-to-end ownership from retrieval/prompt design to scalability, monitoring, and workflow integration via APIs, plus production ML pipeline orchestration with Kubeflow (Spark/Kafka + TensorFlow) for predictive maintenance use cases.”

“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”

“AI/ML engineer with production experience building an LLM-powered resume-to-job matching and feedback product using RAG, with a strong focus on latency, hallucination control, and scalable deployment. Experienced orchestrating ML inference and backend services on Kubernetes and applying rigorous evaluation/guardrail practices; also partnered with business/product stakeholders at Walmart to improve an NLP-based supplier support system.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“HR/people analytics and transformation leader with experience at Royal Caribbean and Baptist Hospital, combining a graduate degree in business analytics with hands-on org design and change execution. Built HR analytics capabilities (predictive/causal approaches, data quality, storytelling) and partnered directly with a CFO to run a 2+ year finance reorg across a 26,000-employee, 14-hospital system, including writing 124 job descriptions and implementing a CoE/business partnering model.”