Vetted A/B Testing Professionals

“Frontend engineer from Meltwater who led delivery of a universal post details modal used across multiple apps, integrating three different frontend frameworks (Vue, React, StencilJS) into a plug-and-play StencilJS feature. Built scalable, reusable UI components and even helped create an internal CSS library/design system, with a strong focus on performance optimization and reliable QA-driven rollouts.”

“Customer Success / client services leader in an expert-network/SaaS environment who owns enterprise accounts end-to-end (onboarding through renewal) and drives land-and-expand growth. Demonstrated measurable impact across renewals (2.5x expansion), rapid user adoption (1 to 20+ users in 3 months), and cross-functional product/data initiatives (refreshing millions of physician profiles to cut delivery time in half and support 125% QoQ growth). Strong in translating customer feedback and campaign analytics into product and workflow improvements that create long-term champions.”

“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”

“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”

“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”

“Hands-on LLM practitioner who built a production document-processing pipeline in Python, tackling long-document handling and latency with chunking/batching and a user-driven correction feedback loop. Experienced operationalizing AI workflows with Kubernetes (CronJobs, autoscaling, scheduled data cleaning and weekly retraining) and applying structured testing/evaluation (E2E, LLM-as-judge, HITL) while communicating solutions clearly to non-technical clients using visual diagrams.”

“AI engineer (2+ years; pursuing an online MS at UIUC) who has shipped an AI-powered voice screening platform end-to-end on GCP with strong production monitoring and measurable hiring-process impact (80% reduction in unqualified pass-through; ~50+ hours saved per role). Also built and deployed an AWS-based context-aware hybrid search system using OpenSearch as a vector store, and has hands-on experience with multi-agent LLM orchestration (ReAct) and structured-output guardrails.”

“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”

“Enterprise CSM with experience at IBM and Cloudera, focused on AI and cloud modernization engagements. Drives adoption, renewals, and expansion by building success plans with measurable ROI/usage outcomes, mediating complex stakeholder priorities, and unblocking deployments through tight Product/Engineering/Sales coordination. Also brings strong GTM analytics capability (Salesforce/HubSpot/GA/Tableau/SQL) to improve funnel performance and pipeline conversion.”

“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”