Vetted Fraud Detection Professionals

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

“Engineering/technology leader with payments (UPI) scale experience who built fraud and growth systems for tens of millions of daily transactions while keeping CAC and cost guardrails. Scaled an engineering org from 12 to 155 in a year using platform + pod structures, and institutionalized canary deployments with auto-rollback (cutting degradations ~75%) while leveraging GenAI for code and test automation.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

“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.”

“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”