Vetted Data Visualization Professionals

Manaswini Gogineni

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development

San Francisco, CA2y exp

“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”

Rashi Agrawal

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud-native microservices

Novi, MI4y exp

“Backend engineer (4 years) who built an end-to-end Python backend for a patent-pending in-car massager/heater system, including GraphQL data modeling and Bluetooth integration with an ESP32 microcontroller (reverse engineered a niche protocol). Also has strong platform experience: on-prem Kubernetes/CI-CD (Jenkins/GitLab, exploring ArgoCD GitOps), Terraform-based infra workflows, a RabbitMQ messaging library used across microservices, and an on-prem migration of ~30 critical applications with rollback/parallel-run strategy.”

Yuan-Hsuan Wen

Screened

Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems

Taipei, Taiwan3y exp

“Built a web-based interface that connects an internal bug system to an LLM for initial debugging and issue classification, aiming to boost QA and software engineer efficiency while balancing latency and accuracy. Worked as a one-person project and managed constraints like limited hardware and difficulty extracting team debugging context, relying on manager communication and rapid modeling to validate direction.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

Prahlad Vivek

Screened

Intern Robotics Engineer specializing in robot learning, SLAM, and control

Wilton, CT3y exp

“Robotics architect intern/new-grad focused on warehouse AMRs, building ROS2 sensor-fusion and SLAM stacks (FastSLAM-style particle filter) and validating in Gazebo with ground-truth metrics. Also interned at ASML debugging real-time in-vacuum robot behavior via Python state-machine telemetry scripts, identifying a firmware driver issue impacting throughput.”

Cathy Shu

Screened

Entry-level Full-Stack Engineer specializing in AI and healthcare applications

Mountain View, CA1y exp

“Early-career full-stack/AI engineer who has already owned a production RAG system for a mental-health-focused startup serving veterans and first responders. Stands out for combining LLM application design, AWS infrastructure work, and product thinking around trust, latency, and guardrails, plus building a separate agentic commerce support prototype and a stock backtesting platform.”

“Machine learning/AI engineer focused on agentic product experiences, including a parts-finding assistant and other AI-driven tools. Has worked on reinforcement learning projects, agent state management, and making AI understandable for non-technical users through visuals and simplified explanations.”

“International development and consulting professional with experience spanning Deloitte, McKinsey, the World Bank, IFC, IDB, and the International Rescue Committee. She has led multi-million-dollar global programs, authored proposals and published research, operated in emergency relief settings across East Africa, and even launched her own startup—bringing a rare mix of strategy, finance oversight, field execution, and high-level stakeholder management.”

“Co-founded academic-index (10,000+ users) and built a full-stack Next.js 14 document upload + client-side OCR + Gemini-powered analysis pipeline with strong production reliability (custom monitoring, retries, quality gates) and measurable gains (accuracy ~94%→98.5%, failures down ~60%). Also owns end-to-end biometric data visualization and a data-driven brand/UX overhaul at pre-seed health/performance startup Absolute Rest, with a background running a multi-client dev studio (Zen Digital).”

Shuvam Mitra

Screened

Mid-level Data Scientist specializing in anomaly detection and production ML

Pittsburgh, PA4y exp

“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”

“Operator with a rare mix of commercial banking, high-growth fintech, SaaS strategy/operations, and CEO office experience. Started in quantitative finance at Citibank, built customer acquisition playbooks at Ramp, and most recently drove cross-functional planning and acquisition integration work at MoneyLion while partnering with senior leadership and the board.”

Michael Kaufman

Screened

Mid-Level Full-Stack Software Engineer specializing in mobile and web platforms

Seattle, WA5y exp

“iOS-focused engineer who led feature development for Amazon Books/Kindle (e.g., Series & Story So Far recaps, Kindle Memories) and introduced pure Swift packages while building sync and content download systems. Also has full-stack experience (React/TypeScript + Node with REST/GraphQL) and strong AWS operations (CDK/CI-CD, CloudWatch, canaries, autoscaling), plus founder experience at GLXY.ai shipping an early hardware MVP (weight sensors) under tight constraints.”