Vetted HIPAA Professionals

“Lifecycle/CRM marketer with hands-on ownership of high-volume, multi-channel programs in Salesforce Marketing Cloud, spanning strategy, build, QA, deployment, and post-campaign analysis. At Bosley Medical, they drove measurable business impact through rigorous segmentation, incrementality testing, and channel optimization, including a campaign that lifted show rate by 19%, consult conversion by 14%, and another messaging test that improved engagement by 22%.”

“Software engineer using AI pragmatically to accelerate development while keeping human review central to quality. Has hands-on experience applying AI and lightweight multi-agent workflows in a microservices environment spanning Java Spring Boot APIs, React modules, and Kafka event flows, with strong emphasis on architecture validation and production safeguards.”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“QA automation engineer who owned an end-to-end, device-integrated mobile regression suite for Tandem’s insulin-pump-connected mobile app, covering pairing/connectivity/data sync and catching contract mismatches and firmware-related connectivity regressions. Strong in Cypress/JavaScript automation, CI gating in GitHub Actions, and diagnosing production-impacting sync issues using logs and API traces while influencing requirements early to improve testability and UX.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”