Vetted Dashboard Development Professionals

Jitesh Kumar S

Screened

Junior Machine Learning Engineer specializing in NLP, computer vision, and MLOps

Lafayette, IN3y exp

“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”

Surya Danturty

Screened

Intern AI/ML Engineer specializing in computer vision and time-series forecasting

Riverside, CA0y exp

“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”

“Former Database Marketing Analyst with hands-on SQL experience supporting targeted casino marketing campaigns and a strong emphasis on validation accuracy. Also describes owning analytics work that standardized engagement metrics across teams, automated cohort/segmentation reporting, and helped identify onboarding friction that improved user engagement.”

David Alvarado

Screened

Junior Business Analyst specializing in data analytics and BI

Orlando, FL3y exp

“Analytics candidate with insurance domain experience at Chubb, combining strong SQL/Python data engineering for claims reporting with business-facing metric design in Power BI. Also built an MLB game outcome predictor that beat Vegas implied probabilities using public data, showing strong product thinking and applied modeling ability beyond standard BI work.”

“Analytics professional with experience spanning financial services and e-commerce, including NAV reconciliation/reporting at State Street and revenue optimization work on checkout and retention. Stands out for combining strong SQL/Python data engineering with business-facing analytics, delivering measurable gains like 3-5% conversion improvement and 8-10% payment success uplift.”

“Lifecycle/CRM marketer with hands-on and strategic experience in fintech membership products at MoneyLion, spanning onboarding, activation, retention, and win-back across email, push, and in-app. Brings strong Iterable and Hightouch execution skills plus prior people management experience from Sync AI, making them a blend of operator, strategist, and team lead.”

“Built both a fantasy sports analytics product and a privacy-sensitive AI assistant for therapists, showing range across consumer and healthcare use cases. Particularly notable for designing self-hosted, HIPAA-conscious LLM systems with RAG, structured outputs, observability, and human-in-the-loop guardrails for clinical workflows.”

Jaeseong Yoon

Screened

Intern-level Software Engineer specializing in full-stack and AI applications

Incheon, Korea0y exp

“Frontend-focused engineer who built Smart Place Analytics, a browser-based facility operations dashboard combining occupancy analytics, alerts, live monitoring, recommendations, and decision history. Stands out for pairing strong UI architecture in Next.js/TypeScript with hands-on browser media work like getUserMedia, canvas capture, throttling, and cleanup to make complex monitoring workflows feel stable and operator-friendly.”

“Senior hands-on full-stack engineer with recent Python/React/TypeScript experience at Terakeet and prior Go-focused backend work at Checkly. Stands out for building and scaling analytics and monitoring platforms in startup-style environments, with strong depth in PostgreSQL performance, containerized deployments, and turning ambiguous business goals into reliable production systems.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Built an AI-based voice interviewer platform at 7C Lingo to automate early-stage candidate screening, owning the full lifecycle from architecture through deployment and weekly production iterations. Implemented a TypeScript/Next.js recruiter dashboard with a Flask/Postgres backend and AWS S3, plus modular services for transcription/analytics/session management using state-driven async workflows. Also created an internal Whisper-powered transcription and editing tool that evolved into a collaborative, versioned, live-transcription system.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”

“Customer-facing technical professional with experience spanning engineering and product who advises on application security tradeoffs (threat modeling, API/auth risks, SOC2 mapping) and drives pragmatic remediation plans. Hands-on with Kubernetes/CI-CD agent integrations, secrets management, and log-driven troubleshooting; documented and escalated complex customer environment issues and reported a 40% reduction in bug reporting through workflow automation.”

“Built and monetized an object-identification app end-to-end (FastAPI backend, HTML/JS frontend, SQLite→Postgres, auth, and an iOS wrapper via Capacitor/Xcode with Apple privacy/policy compliance). Also productionized an AI-native geospatial metadata/QA assistant using LLM+RAG plus deterministic Python validation, measuring impact via time-to-first-pass review and rework rate, and has experience modernizing legacy GIS workflows and delivering across USDA/FEMA-style teams with disciplined Jira-based execution.”

“Built and shipped a production LLM-powered customer support agent for an EV charging platform using RAG plus internal APIs, automating session/payment issues and ticket routing. Emphasizes production readiness via guardrails, schema validation, state-machine orchestration, monitoring, and continuous evals, delivering a reported 35–40% reduction in support tickets and improved customer satisfaction.”

Ponugoti Sushma

Screened

Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML

Texas, USA5y exp

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

Meghana Nandivada

Screened

Junior Machine Learning Engineer specializing in production ML systems and MLOps

2y exp

“ML/AI engineer (TCS) who built and productionized a customer segmentation and personalized-offer recommendation pipeline end-to-end (data cleaning/feature engineering/clustering through Flask API deployment in Docker with monitoring). Emphasizes reliability and operational rigor via validation checks, periodic retraining, model/API versioning, and latency optimization, and has experience translating marketing KPIs into usable dashboards for non-technical teams.”

Dhairya Desai

Screened

Senior AI/ML Engineer specializing in healthcare NLP and predictive analytics

Chicago, IL13y exp

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

Leonard Payne

Screened

Senior Performance Marketing Leader specializing in paid media, lifecycle/CRM, and analytics

Detroit, MI23y exp

“Performance marketer managing high-spend, multi-platform paid media for major brands (e.g., Navistar, Buick), with end-to-end ownership from strategy to reporting. Uses multivariate testing and trend-based budget reallocations to drive measurable lifts (27–60% KPI improvements) and significant outperformance (2.8x vs forecast), and has experience removing conversion friction by replacing lead forms with landing page + chat-based flows.”

“Full-stack/AI product engineer who has shipped both a production-style React finance app and multiple LLM-powered systems end-to-end. Particularly strong in turning early-stage AI concepts into production workflows, including a Bedrock-based multi-turn chatbot with durable session memory and a medical credentialing document parser that cut pipeline failures by 50%+ on large, messy real-world files.”

“Senior product leader who helped transform Submittable from a single-program grant tool into a multi-program impact platform, driving ARR from $20M to $70M+ while improving retention and margins. Particularly strong in enterprise platform strategy and human-centered AI, with a clear philosophy of using AI to augment expert judgment rather than replace it.”