Vetted A/B Testing Professionals

“Wayfair ML/AI engineer who has shipped and operated production LLM systems for both internal analytics and customer-facing assistants. Stands out for combining strong RAG/retrieval engineering with production-grade platform work—improving trust, reducing latency by ~30%, and cutting ad hoc reporting demand by ~50%.”

Omkar Lahurikar

Screened

Senior engineering founder specializing in AI systems and automotive controls

Dearborn, MI13y exp

“Builder-minded founder/operator currently at Ford who previously co-founded MeruDynamics, an "Uber for manufacturing" platform, and took it from zero to paying customer without raising capital. He also independently built and deployed RDD, an AI copilot for engineering decision capture, with evals, prompt versioning, and LLM cost telemetry—showing unusual depth in both product thinking and practical AI execution.”

Sanjana Chowdary

Screened

Intern Full-Stack Engineer specializing in web applications and AI

New York, NY2y exp

“Engineer with hands-on experience both using AI coding agents in production and building AI systems, including chatbot development and BERT fine-tuning at Atos. At Groupr, they applied strong systems judgment to live operational workflows, validating concurrency decisions manually for an admin portal supporting 500+ orders per day.”

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”

Robbie Harol

Screened

Principal Product Leader specializing in AI-native, civic, and climate tech

Los Angeles, CA18y exp

“Product leader with unusually broad experience spanning adtech startups, consumer rewards, political-tech nonprofits, and consulting for operational automation. Stands out for pairing strong UX and monetization instincts with practical AI product work, including building a RAG-based chatbot and consistently using user research to drive measurable outcomes like 400% conversion growth, higher satisfaction, and faster onboarding.”

Vik Krane

Screened

Director-level Product Leader specializing in AI-powered SaaS and FinTech

12y exp

“Product leader and startup co-founder who has been shipping AI-powered products since 2020 across creative tools, expert knowledge products, and financial compliance workflows. Particularly notable for building Shotcraft from zero to scale, using AI to turn scripts into executable shot lists, and for a nuanced human-centered AI philosophy that emphasizes user control over critical outputs.”

“Product leader with a rare mix of healthcare, HR tech, mobile, and AI experience. They rebuilt UPMC's member app for 3M members while pioneering a product-based funding model inside a large enterprise, and later drove 80%+ revenue growth at JazzHR by reshaping marketplace UX for SMB buyers. Their AI work is notably human-centered, with explicit fairness, compliance, and approval guardrails.”

Jared Alonzo

Screened

Mid-level Customer Engineer specializing in enterprise search and technical delivery

New York, NY7y exp

“Search-focused technical consultant/customer success engineer with 6+ years in B2B SaaS, supporting government, higher ed, mid-market, and enterprise customers including Fortune 500 brands like Mondelez, Hasbro, and Sanofi. Particularly strong in enterprise search architecture, personalization data design, and expansion use cases, with hands-on experience shaping solution decisions for retailers such as Snipes and Rue Gilt Groupe.”

Leah Kenehan

Screened

Senior CRM & Lifecycle Marketing Manager specializing in retention and customer growth

Valencia, CA19y exp

“Senior CRM/lifecycle marketer who has grown from email execution into full-funnel campaign ownership, spanning strategy, segmentation, deployment, and performance analysis. Brings hands-on experience with Simon Data, Iterable, and Moveable Ink, plus interim people leadership managing a team of six while driving retention and rebooking campaigns in a travel/hospitality-style DTC environment.”

Michael Zhu

Screened

Mid-level Frontend Software Engineer specializing in React and e-commerce UI

West Hollywood, CA5y exp

“Frontend engineer with strong React/TypeScript depth who has shipped reusable CMS-driven UI architecture and improved production performance by eliminating inefficient per-component API calls. Particularly strong in maintainable component design, pagination/state management, and practical UX optimization using tools like Chrome DevTools and Theme UI.”

Katha Naik

Screened

Intern software engineer specializing in AI, cloud, and full-stack systems

Fremont, CA2y exp

“Engineer with experience at Fox Corporation and Qualcomm, focused on production automation and AI-powered systems. At Fox, they built a serverless Bedrock Operations CoPilot for broadcast/media operations that centralized fragmented operational data and cut incident investigation time by 50-60% across distributed teams and stations. They also bring applied LLM experience from Qualcomm, where they worked on a safer RAG-based learning assistant for children with autism spectrum disorder.”

“Performance marketer focused on high-spend acquisition, having personally owned a ~$500K/month budget driving small business credit card applications for a large US banking client. Experienced running cross-channel programs (Google/Bing search plus LinkedIn/Reddit prospecting) with rigorous experimentation (Drafts & Experiments, bid strategy and match-type tests) and long-horizon measurement to validate sustainable CPA and conversion-rate improvements.”

“Verizon engineer who productionized an LLM-based personalization capability for a customer-facing digital platform, owning the path from success metrics through scalable APIs, A/B validation, and post-launch monitoring (latency/accuracy/drift). Experienced in diagnosing and fixing real-time LLM/RAG workflow issues under peak load, and in enabling adoption via tailored technical demos/workshops and sales support materials.”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“Backend engineer who built an e-commerce order processing service in Python/Flask with PostgreSQL, focusing on correctness and reliability (idempotency, Redis locks, async payment processing with circuit breakers). Also integrated an ML recommendation system as a separate FastAPI inference service with caching and async embedding updates, reporting ~25% CTR lift, and has experience with multi-tenant isolation using PostgreSQL row-level security.”

“Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.”

“AI/ML engineer with healthcare domain depth who led a HIPAA-compliant, production LLM system at McKesson to automate clinical document understanding—extracting entities, summarizing provider notes, and supporting authorization decisions. Hands-on across Spark/Python ETL, Hugging Face + LoRA/QLoRA fine-tuning, RAG, and cloud-native MLOps (Airflow/Kubernetes/Step Functions, MLflow, blue-green on EKS/GKE), with explicit work on PHI handling and hallucination reduction.”

“Built an end-to-end Alexa smart-home IoT application controlling a Wi-Fi bulb, including ESP32 firmware (MQTT) and an AWS serverless backend (IoT Core/Device Shadow, Lambda, DynamoDB) with a REST API. Demonstrates strong real-time scalability patterns (streaming ingestion, stateless processing, partition-key design) and full-stack delivery with Spring Boot + React (JWT auth, CORS, data-heavy dashboards).”

“Built and deployed a GPT-based RAG enterprise search system for healthcare clinicians, emphasizing low-latency performance and reduced hallucinations while maintaining end-to-end HIPAA compliance. Demonstrates deep applied experience with PHI-safe data governance (detection/redaction/de-identification), secure Azure ML deployment patterns, and orchestration of production LLM workflows using LangChain and Airflow.”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

Yaswanth Thota Thota

Screened

Mid-level Data Analyst specializing in financial risk and healthcare analytics

AZ, USA4y exp

“AI/ML engineer focused on real-time, production-grade LLM systems, with a robotics-adjacent mindset around latency/accuracy tradeoffs and modular pipelines. Built a scalable RAG-based assistant orchestrated as microservices on Kubernetes with Kafka async messaging, ONNX/quantization optimizations, and monitoring (Prometheus/Grafana), citing a ~35% hallucination reduction; has also experimented with ROS Noetic/Gazebo to understand ROS concepts.”