Vetted Generative AI Professionals

Steven Fritsch

Screened

Principal Product Manager specializing in AI and document intelligence

New York, NY12y exp

“Enterprise product leader with significant experience turning AI experiments into scalable, workflow-native capabilities at Datasite, especially in high-stakes M&A environments. Stands out for combining strong AI product strategy with nuanced UX judgment, emphasizing trust, transparency, and human-in-the-loop design over flashy automation.”

“Lead full stack Java developer with deep experience building compliance-sensitive government financial systems, including payment distribution, escrow hold management, reallocations, and disbursement workflows. Stands out for combining Angular/React frontend work with Spring Boot, Oracle SQL/PL-SQL, and batch/JMS architecture to stabilize high-volume transaction systems, automate manual workflows, and reduce production support issues.”

“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“Built production LLM tooling focused on reproducibility and verification by enforcing JSON schemas and using multi-step checks with tools like Firecrawl and Perplexity. Also implemented the containerized infrastructure layer for a 9-agent app on K3s, dealing with rolling updates and uptime, and has experience advising a non-technical builder on search grounding and LLM data-flow design.”

“Infrastructure/platform engineer with hands-on ownership of on-prem OpenStack/VMware and Kubernetes clusters deployed via Kubespray, including OpenStack-integrated networking and Ceph-backed storage. Built a GitOps-style Terraform delivery model in GitLab with CI/CD and security scanning (SAST/DAST/Checkov), and operated a hybrid on-prem/AWS ecommerce architecture with PCI-segmented payment systems over site-to-site VPN.”

Sri Vyshnavi Maganti

Screened

Senior Business Analytics Analyst specializing in product and customer analytics

Texas, USA7y exp

“Darwinbox team member who supported talent/recruiting operations while also driving product improvements across HR modules (recruitment, onboarding, payroll, performance). Led a small team (5–6) and implemented discovery-driven configuration and BI reporting (Power BI/Tableau/Confluence), including a reported 30% reduction in recruitment configuration issues and real-time funnel reporting to support fast hiring.”

Archana yaramala

Screened

Mid-level AI/ML Engineer specializing in deep learning, MLOps, and LLM applications

NY, USA4y exp

“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“Research-heavy ML/AI candidate with a PhD/publications background who translated LLM evaluation and clinical summarization techniques into production at ModMed. They owned an end-to-end healthcare GenAI pipeline that cut clinician documentation time from ~22 minutes to ~7-8 minutes, reduced token costs by ~30%, and built an internal evaluation framework later adopted by multiple teams.”

John Chance

Screened

Senior Machine Learning Engineer specializing in conversational AI and healthcare ML

Greenwood, LA9y exp

“ML/AI engineer with hands-on ownership of both classical recommender systems and safety-sensitive LLM agent platforms. They combine production MLOps depth with behavioral health domain experience, including clinical safety validation, explainability, and multi-agent orchestration, and cite measurable impact in both business metrics and latency reduction.”

“Software engineer currently building AI-powered backend systems for interview analysis, with end-to-end ownership of an LLM-based monitoring platform. Stands out for combining practical product delivery in an ambiguous early-stage environment with measurable impact: over 40% reduction in manual review effort and roughly 20% lower inference cost.”

“AI-native software developer who has built a highly structured workflow around Claude, Cursor, design agents, and SpecKit to plan, design, implement, and test features end to end. They also use multi-agent setups with sub-agents and git worktrees, and have experience acting as a tech lead for AI agents by assigning roles, guiding execution, and reviewing outputs.”

Jalaan Fields

Screened

Mid-level finance and strategy analyst specializing in valuation and performance analytics

Remote7y exp

“Co-founder of ATW Labs who built a consulting and analytics pipeline from scratch, selling into middle-market companies and converting outreach into repeat client engagements. Brings a blend of founder empathy, executive-facing business development, and early conviction around practical generative AI use cases, with experience translating ambiguous client needs into high-ROI recommendations.”

“Backend engineer who built the core backend for Air Kitchens’ discovery/booking platform on AWS (Node + Python, DynamoDB, SQS/Lambda), optimizing for fast user-facing APIs and scalable async workflows. Introduced an AI matching service with a deterministic pre-filter + LLM ranking approach to balance latency vs quality, and has hands-on experience with production security (JWT/RBAC/RLS), CI/CD, and blue-green, staged migrations from Django to modular services.”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.”

“Built a production multi-agent orchestration platform to automate healthcare claims and HR workflows, combining LangChain/CrewAI/AutoGPT with RAG (FAISS/Pinecone) and fine-tuned open-source LLMs (LLaMA/Mistral/Falcon) in private Azure ML environments to meet HIPAA requirements. Emphasizes rigorous agent evaluation/observability (trajectory eval, adversarial testing, LLM-as-judge, drift monitoring) and reports measurable outcomes including 35% faster claims processing and 40% fewer chatbot errors.”