Vetted Fraud Detection Professionals

Krishna Guda

Screened

Principal Software Engineer specializing in AI-native FinTech systems

Mountain View, CA23y exp

“Fintech product engineer working on a large-scale credit monitoring platform (tens of millions of users) with deep experience in regulated banking integrations, PII security, and step-up/MFA flows. Has shipped customer-facing React/TypeScript experiences driven by Optimizely experimentation and built reliable partner-facing microservices/SDKs on AWS, including resolving production traffic loss caused by edge security (DataDome/CAPTCHA) conflicts with payment providers.”

“Student founder/operator with hands-on startup experience spanning GTM, CRM automation, AI dashboard building, and early customer development. At Doppelganger Foods, they built automated systems that surfaced $242K in revenue opportunities, and in their own venture, Startup Foundry, they secured 2 school pilots and onboarded 150+ founders.”

“Enterprise implementation leader with 10+ years delivering multi-million-dollar B2B/SaaS rollouts across procurement, expense management, legal tech, and fraud/identity platforms. At Amazon Business, managed 12+ concurrent enterprise implementations and owned the full lifecycle from strategy and change management through go-live, with strong executive stakeholder management and governance discipline.”

Ben Kleiman

Screened

Executive product leader specializing in AI, data platforms, and national security

Denver, CO17y exp

“Head of Product at Bitly who built the company's Trust & Safety strategy and led a patent-pending abuse-detection platform that intercepted over 700 million harmful clicks. Also launched Bitly Assist, an LLM-powered assistant designed around human control and workflow integration, showing unusual depth in both AI product strategy and high-stakes safety/governance.”

Pravesh Garg

Screened

Senior Product Manager specializing in AI, data, and digital products

New York, NY8y exp

“Product-focused candidate with experience applying AI to automate internal audit workflows, including OCR, prompt-engineered control testing, and report generation that saved 2000 manual hours annually. Also led UX design for a 30-minute retail delivery experience and used A/B testing, partner feedback, and KPI analysis to improve on-time delivery.”

“Engineering-management graduate student at Cornell who has grown from quality engineering into product leadership, with experience spanning telecom, accessibility operations, and an AI healthcare fraud startup. Particularly compelling for PM roles requiring ambiguity handling, cross-functional execution, and thoughtful tradeoffs between AI capability, explainability, cost, and regulatory constraints.”

“Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.”

“Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.”

“Backend/platform engineer building HIPAA-compliant, real-time healthcare systems: owned a Python/Flask API layer for an AI-enabled patient engagement and risk scoring service, implemented PHI-safe logging and cross-service auditability, and delivered Kubernetes microservices via ArgoCD GitOps. Also has experience with Kafka streaming pipelines and hybrid cloud-to-on-prem migrations in regulated healthcare/fintech environments.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

Geetha Sri Kasu

Screened

Mid-level Software Engineer specializing in Java/Spring Boot, Kafka, and AWS

5y exp

“Software engineer who owned an end-to-end self-service reporting workflow (secure APIs, async/batched processing, and React UI), improving report generation performance by ~30–40% and reducing manual support effort. Also built a RAG/embeddings prototype over internal docs and service logs with grounding-focused guardrails, and has a strong reliability/observability mindset (retries, DLQs, CI/CD validation, dashboards/tracing) for distributed workflows.”

Abhirup Chakraborty

Screened

Junior Software Engineer specializing in backend systems, AI, and search

Champaign, IL3y exp

“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”

“Product leader with 12 years of experience spanning consumer, platform, and data-governance products. Most notably built a Content Security platform at Disney from a vague concept into a delivered Salesforce-based product with centralized data, orchestration, auditing, and watermarking modules, and has a track record of navigating complex stakeholder and technical constraints.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”