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Vetted Data Visualization Professionals
Pre-screened and vetted.
Senior Client Solutions & Customer Success Leader specializing in Enterprise AdTech and B2B SaaS
Executive Engineering Leader specializing in AI and Financial Services platforms
Staff Software Engineer specializing in FinTech eCommerce platforms
Staff-level Full-Stack/Platform Engineer specializing in cloud, serverless, and AI search
Senior Backend/Platform Software Engineer specializing in data systems and API integrations
Senior Full-Stack Engineer specializing in cloud-native SaaS and AI/ML integration
Mid-level SDET specializing in embedded firmware validation and test automation
Senior Full-Stack Engineer specializing in microservices, data pipelines, and AI in FinTech
Senior Data Scientist specializing in AI/ML platforms for finance and healthcare
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Senior Talent Acquisition Operations leader specializing in global recruiting technology and AI enablement
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Director-level Business Operations leader specializing in FinTech and AI-enabled operations
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Senior Full-Stack Software Engineer specializing in cloud platforms and AI integration
Junior Data Scientist & Data Engineer specializing in ML and scalable data pipelines
Danny Klein
Intern Robotics Engineer specializing in robotics testing, controls, and automation
“Robotics engineering intern and mechanical engineering master’s student who bridges hardware testing and ML/ROS2 software: built a PyTorch model to map motor test data across motor types using electrical specs (Kv/Kt/R/L) and validated it against new motors to meet strict torque/thermal accuracy targets. Also integrated CNN-based perception into ROS2 for real-time navigation and implemented MPC with time-synchronized multi-topic messaging to avoid stale-data control issues.”
Ethan Pribble
Senior Software Engineer specializing in cloud cost intelligence and FinOps platforms
“Backend/data engineer with strong authorization and compliance-domain experience: led a phased migration from a simplistic role model to modern RBAC on a Python serverless stack (Auth0 + AWS Lambda/API Gateway), coordinating changes across 5 repos with extensive manual and automated validation. Previously built and operated custom ETL pipelines (Airflow + Groovy/Java on Spark/YARN/Hadoop) to normalize messy customer email/chat/voice data for NLP-driven financial compliance indicators, including complex email journaling metadata enrichment and large-scale remediation reprocessing after production bugs.”
Yeshwanth Sai Pala
Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech
“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”
Ryan Crabbe
Junior Full-Stack/Mobile Engineer specializing in React Native and NestJS
“Built an AI-powered restaurant menu rewriting app that generates diet-constrained menus from photos, with a backend designed around bounded contexts and a lightweight CQRS approach. Demonstrates strong multi-tenant PostgreSQL design (RLS, tenant-scoped queries) and performance tuning (partitioning, keyset pagination, composite/partial indexes), plus AI workflow orchestration using Redis/BullMQ and Vercel AI SDK with structured outputs and evals; reduced p95 latency ~35–50% via racing LLM requests and caching.”
Dheeraj Kumar
Intern Data Scientist specializing in marketing analytics and data engineering
“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”