Vetted Feature Engineering Professionals

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“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。”

“LLM engineer who built a production seller-support RAG system at eBay using hybrid retrieval (BM25 + Pinecone vectors) with Cohere reranking, LangGraph orchestration, and citation-grounded answers. Strong focus on reliability: semantic/structure-aware chunking, automated Ragas-based evaluation with nightly regressions, and production observability (LangSmith) plus drift monitoring (Arize). Also implemented a multi-agent fraud pipeline with AutoGen using JSON-schema contracts and explicit termination conditions.”

“LLM engineer who has deployed production RAG systems for regulated document QA (PDFs/knowledge bases), emphasizing grounded answers with citations, RBAC, monitoring, and continuous feedback. Demonstrates deep practical expertise in retrieval quality (semantic chunking, hybrid BM25+embeddings, re-ranking), reliability (guardrails, deterministic workflows), and measurable evaluation (golden sets, log replay, A/B tests) while partnering closely with compliance/operations stakeholders.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“Backend/ML engineer who built production LLM-enabled systems at PRGX, including an interpretable contract opportunity scoring engine (Bradley-Terry pairwise ranking) that reached 0.82 weighted Spearman agreement with SME auditors and was integrated into workflow. Also built a Duke student advisor chatbot and hardened it for real-world reliability/security with schema-driven tool calling, normalization, and off-domain defenses; led staged production rollouts with shadow testing and achieved 0.90 F1 on a new extraction field before shipping.”

“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.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“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).”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“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.”

“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.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”

“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).”

“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.”

“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.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”