Vetted Forecasting Professionals

“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”

“Partnerships and growth leader (Traveronto) specializing in partner-led GTM through enterprise/API integrations and white-label distribution. Uses rigorous analytics (audience overlap, engagement quality, cohort retention/LTV) to source and scale creator/platform partnerships, and runs funnel-driven A/B tests on onboarding and pricing to improve activation, GMV, and recurring revenue while keeping CAC low.”

“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”

“Candidate states they have 20+ years of experience in government relations and govtech, but declined to provide details on outbound sales/business development motions, prospecting, outreach campaigns, early-stage process building, or AI/automation usage during the screening.”

“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.”

“Operations leader with deep fintech/lending startup experience (OnDeck Capital, 1st Merchant Funding, ForwardLine, Founders First) building end-to-end underwriting-to-portfolio operations, compliance, and supporting functions like sales/marketing. Known for scaling teams and automating repeatable work—citing growth from ~$3MM to ~$20MM in 2 years—while implementing systems like Salesforce/QuickSight and helping build proprietary funding and portfolio management software. Also provides structured part-time advisory support with clear scope/cadence, including helping set up a global finance company based in Argentina.”

“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.”

“Sourcing/procurement professional focused on electronic components, leading end-to-end RFQ and supplier selection through negotiation and delivery. Demonstrates strong data-driven cost management (Power BI modeling, benchmark pricing) and measurable results including ~18% cost reduction, avoiding production delays during shortages, and automating RFQ comparisons to cut cycle time ~40%.”

“Verizon sales development professional supporting two mid-market AEs, running weekly Salesforce funnel reviews and advancing deals involving 10–250 lines and buying committees from IT manager to VP. Drives results through high-volume, multi-channel outbound sequences (calls, email, text, LinkedIn), consistently exceeding 120% quota and generating $200K+ in quarterly revenue while maintaining ~100 active opportunities.”

“Salesforce-focused implementation/solutions engineer from Full Circle Insights who owned end-to-end campaign attribution and reporting deployments for multiple customers at once (3–5 concurrently), including sandbox testing, KPI monitoring, and rollback-safe migrations from legacy reporting. Also builds personal multi-agent workflows and uses Claude Code to rapidly scaffold data/analytics scripts like an advertising optimization parser over CSV/XLSX inputs.”

“CTO candidate who independently built an AI bot to assist store associates by first analyzing manual workflows, then delivering a nights/weekends POC that earned executive sponsorship and immediate funding. Successfully scaled the initiative to production by reallocating developers and later secured budget for a dedicated FTE within four months, citing strong adoption and cost savings.”

“Global Talent Acquisition/Recruiting Operations leader who has scaled and standardized end-to-end recruiting across regions and large teams (5–95), including major ATS/HCM implementations (Workday, Lever, Greenhouse, BambooHR). Known for rebuilding “Frankenstein” recruiting orgs into measurable operating models—cutting time-to-fill 28%, improving forecast accuracy to 5–10% variance, and boosting hiring manager satisfaction by 30+ points—while building offshore sourcing capability (South Africa delivering 55–60% of early funnel).”

“Recruiting Operations leader with 10 years across transportation, staffing, and tech, managing teams up to 40 in both remote-first and onsite settings. Known for rebuilding end-to-end recruiting workflows (interviewing, pay equity/salary transparency, onboarding automation) and delivering measurable outcomes including 140% revenue growth, plus strong ATS analytics and HRIS/LMS implementation experience (Workable, SmartRecruiters, Paycom, Litmos).”

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

“Talent Acquisition/Recruiting Operations leader who owned end-to-end recruiting ops and served as the Workday TA implementation SME at DART, standardizing a fragmented hiring process into documented SOPs, dashboards, and SLAs that cut time-to-fill by 52% in one year. Previously led recruiting/resource/payroll teams supporting 10k+ independent catastrophe adjusters, building surge workflows and compliance checkpoints to staff disaster events within hours.”

“Exited founder who spent 12 years building and operating The Konery, scaling it from a parents’ kitchen into a nationally distributed CPG manufacturing business in Brooklyn and selling it in 2025. Deep hands-on operator with end-to-end CEO experience, including doubling capacity through machinery implementation and running SQF certification for five years with two perfect audits.”

“Built and deployed a production LLM-powered RAG assistant for healthcare teams (care managers/support) to answer questions from clinical and policy documentation, emphasizing trustworthiness via improved retrieval, reranking, and strict grounding prompts to reduce hallucinations. Also has hands-on orchestration experience with Apache Airflow for end-to-end ETL/ML workflows and applies rigorous testing/metrics (hallucination rate, tool-call accuracy, latency, cost) to ensure reliable AI agent behavior.”

“Early-career engineer from BNP Paribas who drove a large-scale observability modernization—selecting and implementing Prometheus/Grafana for a 2000+ server estate, then productionizing it on Kubernetes via Docker/Jenkins. Known for hands-on demos, strong documentation/templates, and pragmatic troubleshooting (including custom Python metrics) that improved visibility and cut debugging time by ~60%.”

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“Built production LLM/RAG platforms for financial services to enable natural-language Q&A over large policy/compliance document sets stored in Snowflake and SharePoint. Strong in MLOps and orchestration (Airflow, ADF, Step Functions, MLflow) and in solving real production issues like stale embeddings and model performance, including an incremental Snowflake Streams sync that cut processing time from hours to minutes.”

“Built a production internal LLM/RAG assistant at CVS Health to cut time spent searching long policy and clinical guideline PDFs, combining fine-tuned BERT/GPT models with FAISS retrieval and a FastAPI service on AWS. Demonstrates strong real-world reliability work (document cleanup, hallucination controls, monitoring/drift tracking with MLflow) and close collaboration with non-technical clinical operations teams via demos and feedback-driven iteration.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”