Vetted A/B Testing Professionals

“Built a chatbot for a learning management system during a Deep Atlas bootcamp by mapping an end-to-end RAG architecture (document ingestion, Qdrant-based retrieval scoring, and LLM response synthesis). Previously at Rally Health/UnitedHealthcare, diagnosed load-related memory spikes with JMeter and improved stability by migrating caching from Guava to Redis, and also supported adoption through UI A/B testing in a technical marketing engineer rotation.”

“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”

“Software engineer at Fannie Mae (~3 years) working on high-volume loan data pipelines using AWS (SQS/S3), Java listeners, Postgres, and Python/SQL-based data quality validation. Also built a chess data collection system (leveraging experience as an International Master) with robust retry/monitoring, schema-change handling, and idempotent backfills to prevent bad data from reaching downstream systems.”

“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”

“Built and deployed a production RAG-based semantic search and summarization system for large legal/technical document sets, owning the full backend (embeddings, vector store, chunking, prompting) and driving a reported 40–60% reduction in manual review time. Experienced with LangChain/LlamaIndex plus Airflow/Temporal-style orchestration, and applies rigorous evaluation/monitoring (A/B tests, drift detection, staged rollouts) to keep agentic systems reliable. Also partnered with a supply-chain manager at TE Connectivity to deliver an AI inventory recommendation tool projected to drive millions in value.”

“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”

“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”

“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”