Vetted Data Visualization Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

Dhananjay Sharma

Screened ReferencesModerate rec.

Mid-level Full-Stack Engineer specializing in AI and FinTech platforms

Stony Brook, NY6y exp

“Full-stack engineer who built RegArt’s product from 0→1 for enterprise compliance users at clients like HSBC and EY, including the production React frontend, backend APIs, and an LLM-powered search experience. Particularly compelling for startups needing someone who can move across UI, API, and data layers, make pragmatic architecture tradeoffs, and ship fast without over-engineering.”

“Candidate comes from SaaS and healthcare analytics rather than game development, but has strong end-to-end ownership experience building real-time, high-availability systems in Python/AWS. They highlight measurable impact across performance, throughput, uptime, and cost reduction, including queue optimization and predictive ICU utilization pipelines, and are looking to transfer that systems engineering foundation into Unity/gameplay work.”

“Supply chain/sourcing professional focused on semiconductor tooling NPI ramp-ups, owning vendor selection through production and delivery. Demonstrates strong cost and trade-risk mitigation (e.g., avoided 25% Section 301 tariff via HTS/landed-cost analysis) and measurable supplier performance turnarounds (OTD improved to 95%) using SAP MRP, shortage analysis, and structured supplier governance.”

“Embedded/real-time robotics-style engineer with hands-on STM32 development, sensor integration, and low-level drivers, focused on deterministic control behavior. Demonstrated systematic debugging of jitter/latency by instrumenting the sensing-to-actuation pipeline and eliminating blocking via interrupts, hardware timers, and DMA; also designs asynchronous, message-based interfaces for distributed real-time components. Familiar with ROS/ROS2 concepts (nodes/topics/callbacks) though not yet deployed a full production ROS system.”

“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”

“Paid media specialist with hands-on ownership of high-spend digital audio, including a $75K/month Progressive campaign run in Spotify (trafficked via Megaphone and Google Ad Manager). Demonstrates a structured full-funnel approach across Meta/TikTok/Google and a rigorous testing and incrementality mindset (marginal CPA/ROAS, saturation signals, new-user contribution, branded search lift) to guide optimization and scaling.”

“Program/operations leader with Booz Allen Hamilton experience turning around a near-canceled Millennium Challenge Account Indonesia procurement-training contract by implementing train-the-trainer, mentorship, and KPI/dashboard-driven execution. Currently supports the CFO organization at Berkeley Lab, translating high-level strategy into people/process/technology/governance plans with an executive operating cadence.”

“Frontend engineer with senior-level scope who has shipped complex React/TypeScript products end-to-end, including a multi-page site built solo in four weeks (complex forms, PDF generation, email + localization) and a GraphQL-driven dashboard for a car dealership management platform. Known for modular, testable architecture, performance-minded data fetching (batched calls), and improving design-system consistency via reusable, Storybook-documented components and feature-flagged rollouts.”

“LLM/agentic workflow builder focused on productionizing document-processing systems. Redesigned pipelines with LangGraph + RAG, schema-aware validation, and eval/monitoring loops; known for fast incident diagnosis (restored accuracy from ~70% to >95% same day). Partners closely with sales and stakeholders to deliver tailored demos and drive adoption (reported +40%).”

“Public-sector-focused consultant with hands-on ownership of outbound/ABM, lead gen, and GTM strategy for early-stage and pre-launch products (including edtech). Built multi-channel campaigns spanning SEO/web copy, email, conferences, and direct outreach to state/district leaders, and created an Airtable onboarding system that cut prospect research time by 33%.”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Full-stack engineer who built and owned a production real-time computer-vision inference platform at Credence, spanning Next.js App Router/TypeScript frontend with SSE/WebSocket streaming, a Flask backend, and Postgres analytics. Demonstrated measurable performance wins (70% fewer re-renders; latency cut to ~40–50ms) and strong production rigor (durable orchestration, idempotency, observability, AWS EC2 + CI/CD) with tight post-launch UX iteration based on analyst feedback.”

Dylan Tang

Screened

Intern Software Developer and ML Researcher specializing in medical imaging and computer vision

Chicago, IL0y exp

“AI/ML practitioner with experience spanning audio/LLM applications (built "Iota" using Whisper, tiktoken, and a local Ollama-served LLM) and healthcare ML (Facemed.ai; UChicago Radiology). Demonstrates a production-oriented mindset—focus on data/model fit, deterministic field testing, and operational safeguards—and has improved research evaluation workflows via a hash-table-based concurrent model tracking approach.”

Caroline Cahill

Screened

Senior Graphic Designer specializing in brand campaigns, marketing design, and art direction

New York City, NY4y exp

“Marketing creative/art director and designer with portfolio work for Glossier, Vox Media, and Bobbi Brown, focused on premium, modern brand storytelling. Builds scalable Figma systems and modular templates for high-volume cross-platform campaigns (social/paid/e-comm) and pairs them with a fast, template-driven video workflow, iterating based on performance signals while maintaining brand guardrails in fully remote async collaboration.”

Shrey Kumar

Screened

Junior Full-Stack Software Developer specializing in web platforms and AI workflows

Dallas, TX1y exp

“Software engineer who built and shipped “Counsellor AI,” a production LLM-powered academic advising agent for college students using AWS Bedrock and grounded RAG over official university catalogs/policies. Emphasizes reliability through structured JSON outputs, multi-step orchestration with shared state, and strict intake/validation gates to prevent hallucinations and invalid academic plans; also has experience hardening messy telecom operational data pipelines with normalization, permissions, fallbacks, and idempotent patterns.”

Shravya Shashidhar

Screened

Intern Software Engineer specializing in LLM agents and full-stack development

Seattle, USA1y exp

“Embedded C++ engineer with Bosch automotive infotainment experience, owning real-time audio middleware modules with strict latency/memory constraints. Strong in profiling/optimizing deterministic behavior, debugging hardware-specific intermittent issues, and building automated test + CI pipelines; currently ramping up on ROS2 concepts (DDS, nodes/topics/services) to transition toward robotics.”

“Analytics-focused candidate with hands-on experience building SQL data pipelines and Python-based forecasting workflows for inventory and planning use cases. They emphasize data quality, stakeholder trust, and operational adoption, citing a 19% forecast accuracy improvement and strong experience translating analytics into dashboard-ready business metrics.”

“Finance data and reporting professional with PwC experience who bridges accounting and technology, especially around GL-related reconciliations, reporting accuracy, and close support. While not a direct PeopleSoft GL owner, they bring strong SQL-driven troubleshooting, ETL/data mapping remediation, and process automation experience that helped shorten close cycles and improve audit readiness.”