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Vetted A/B Testing Professionals
Pre-screened and vetted.
Osvaldo Calles
Senior Software Engineer specializing in developer tools, cloud automation, and generative AI
“Built and deployed a production chatbot on osvaldocalles.com and iterated through real-world LLM engineering issues: model quota/cost tradeoffs (migrating to Nova Pro), RAG accuracy via semantic chunking, AWS IAM/guardrail/security pitfalls, and Lambda/API Gateway streaming constraints (prefers JS for streaming layer). Experienced with agent orchestration using Strands SDK (AWS-focused) and LangGraph (Vercel/container deployments), plus evaluation pipelines using LLM-as-evaluator, dashboards, and staged model rollouts.”
Jared Hoffen
Senior AI Engineer specializing in LLM agents, RAG, and ML infrastructure
“Production-focused AI/ML engineer who has owned LLM agent and RAG systems end-to-end, from experimentation through deployment, monitoring, and iterative optimization. Stands out for building evaluation and observability layers around GenAI systems and delivering measurable gains in task success, regression detection speed, and token efficiency in production.”
Shailaja Domala
Executive product and data leader specializing in AI, analytics, and FinTech platforms
“Senior product leader with 15 years of people management experience who has built AI-driven products from 0 to 1, including a no-code ML platform for citizen data scientists and data/insight products at Visa. Brings a rare mix of fintech, AI/ML, UX, and platform thinking, plus a strong human-centered AI perspective shaped by ethical AI work and mentoring underserved college graduates in India.”
John Bralich
Senior Machine Learning Scientist specializing in generative AI and applied NLP
“ML/AI tech lead who shipped a production LLM workflow at GoDaddy for personalized marketing content, using rich customer context and human-plus-LLM evaluation to drive a statistically significant increase in customers creating posts with GoDaddy tools. Also has experience translating embedding research into a production government RFP search engine, with hands-on optimization of retrieval latency, model size, and deployment reliability.”
Ritesh Somashekar
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”
Avinash Pittu
Mid-level Software Engineer specializing in ads, full-stack systems, and AI automation
“Meta engineer who emphasizes AI-native development workflows, using Claude Code heavily to ship UI and performance fixes quickly. Notable examples include a location-aware ad relevance feature that increased CTR and revenue, and a vehicle insights chatbot whose UX improved through metric-driven prompt tuning.”
Parth Parikh
Senior Software Engineer specializing in backend systems and AI platforms
“Engineer with experience at Reddit working on high-scale backend and infrastructure problems, including API redesign for products serving 150M+ daily active users. They also built a production AI agent for automated bug triage with 97% accuracy and substantial time savings, and have hands-on full-stack/AI side-project experience using React, TypeScript, Supabase, and LLMs.”
John Powell
Senior Software Engineer specializing in AI/ML platforms and healthcare systems
“Unity/C# gameplay engineer with strong systems architecture depth who has reworked core gameplay ability frameworks, shipped across mobile and standalone VR, and solved multiplayer synchronization issues with server-authoritative netcode. Also brings an unusual crossover into AI tooling, having owned an AI-powered debugging assistant at Arm and integrated LLM workflows into CI/development pipelines.”
Tanmayee Chandanam
Mid-level Data Scientist specializing in recommender systems, NLP, and real-time ML pipelines
“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”
Chenghui Cai
Director of Applied Sciences specializing in reinforcement learning and agentic AI for finance
“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”
Dhruv Arora
Senior Generative AI Implementation Consultant specializing in RAG and agentic AI on cloud
“LLM/RAG practitioner who built an AWS-based enterprise document search and summarization platform with RBAC and scaled it to 10K+ users, solving relevance issues via contextual chunking and hybrid retrieval. Also designed agentic workflows for a telecom forecast-validation use case using sub-agents, tool APIs, and strict context management, and has proven pre-sales influence (supported a $300K manufacturing deal with a roadmap-driven pitch).”
PoHung Chen
Junior AI/ML Engineer specializing in MLOps and real-time model serving
“Software engineer with Amazon experience who has built LLM-powered and hybrid ML systems for ad auction/relevance at massive scale. Most notably, they described redesigning brand-query classification with a GPT-4-assisted offline cache plus fallback architecture that improved accuracy from 72% to 99%, reduced latency and costs, and was credited with an estimated $130M revenue lift.”
Ujaas Barvalia
Principal Data Scientist specializing in machine learning and generative AI
“Atlassian ML/AI engineer who has shipped end-to-end production systems combining classical ML, streaming infrastructure, and LLM-based personalization to improve onboarding and free-to-paid conversion. Particularly strong in turning research-style RAG and reranking ideas into low-latency, reliable product systems with robust evaluation, safety guardrails, and reusable platform services for other teams.”
Jonathan Wang
Senior Software Engineer specializing in platform, authentication, and developer infrastructure
“Software engineer who has deeply integrated AI into day-to-day development, using Claude Code, ChatGPT, and coding agents to speed up boilerplate generation, system design, and tradeoff analysis. Stands out for a pragmatic multi-model workflow focused on faster delivery and quicker architectural feedback.”
Ami Foelz
Principal Product Leader specializing in AI personalization, search, and e-commerce
“Product leader with experience building product organizations from scratch at both Amazon and LTK, with a strong focus on AI-powered search, discovery, personalization, and monetization. They combine strategic product leadership with hands-on UX and technical fluency, and have delivered measurable business impact including a 17% conversion lift and 21% revenue growth at LTK.”
Chandra sai kiran Kammari
Mid-level Machine Learning Engineer specializing in fraud detection and real-time personalization
“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”
YAKKALI PAVAN
Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems
“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”
Keerthana Senthilnathan
Junior Machine Learning Engineer specializing in LLM systems and inference reliability
“ML/LLM infrastructure-focused engineer who built a production stateful LLM inference service that cuts latency and GPU compute for repeated/overlapping prompts via caching with correctness guardrails. Strong in Kubernetes-based deployment and reliability engineering, using A/B testing and similarity-based evaluation to quantify performance gains without sacrificing output quality.”
Rohini Rajagopalan
Director-level Engineering Leader specializing in SaaS, Cloud Migration, and Cybersecurity
“Senior engineering leader with experience at Cisco, Amazon, and startup Shopkick, operating at high scale (e.g., Secure Web Gateway handling ~40M QPS). Known for measurable impact across reliability and cost (85% efficacy improvement; Datadog spend cut from ~$500k/month to ~$15k/month) and for leading complex platform modernization (1-year monolith-to-microservices/event-driven migration with zero customer impact) plus compatibility-focused API design that cut device onboarding from a month to a day.”
Durgaprasad G
Mid-level AI/ML Engineer specializing in LLM infrastructure, RAG, and agentic systems
“Stripe engineer who owned and unified multiple team RAG systems into a shared production platform used by 200+ internal operators, deployed on EKS with Kafka ingestion and hybrid retrieval. Drove measurable business outcomes including <400ms latency, ~35% inference cost reduction, ~25% accuracy lift via fine-tuning, and real-time auto-approval of 80%+ merchant compliance applications through strong observability and reliability patterns.”
Dawn Siegel
Executive Marketing & Media Operations Leader specializing in paid media and operational excellence
“Performance marketer with hands-on ownership of a high-spend ($50K+/month+) financial services account running integrated campaigns across paid search, paid social, and programmatic (Google/Microsoft, Meta, TikTok, DV360, The Trade Desk, Amazon Ads, and more). Experienced driving new account openings against cost-per-open targets through audience/creative testing, sequential messaging and influencer-led upper funnel, and rigorous tracking/measurement in a highly regulated environment.”
“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”
Angel Contreras
Senior Data Scientist specializing in machine learning, NLP, and MLOps
“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”
Soma Meghana Prathipati
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”