Vetted Dashboard Development Professionals

“AI/full-stack builder with a track record of shipping practical LLM products in both hackathon and professional settings. Built ScoutR, an agentic football scouting platform that won Best Use of Gemini at HackCU 2026, and at Merkle shipped a GPT-4-based review-tagging tool that cut analyst tagging time by 90%.”

ERIC Beecroft

Screened

Mid-level Full-Stack Engineer specializing in frontend-led FinTech products

8y exp

“Full-stack engineer with leadership experience across fintech and data products, spanning React/TypeScript frontends, Kotlin/Spring and .NET backends, and AWS infrastructure. Stands out for pairing strong product judgment with hands-on execution: they introduced safer progressive rollout patterns, led Google Auth implementation, and contributed to a lending flow that drove an additional $1M in funded loans in 30 days. Recently expanded into AI tooling by building an MCP app for Claude Desktop integrated with internal APIs.”

“Robotics software engineer with a Master’s thesis building an end-to-end monocular-vision pick-and-place controller for construction use cases on TurtleBot3 + OpenManipulator, spanning synthetic data creation, transfer learning, simulation in Gazebo, and real-robot deployment. Leveraged ROS distributed architecture to run two heavy AI models across networked GPUs to achieve usable real-time performance, and has production CI/CD experience as a Senior Software Engineer in AI/analytics.”

“LLM/agentic systems builder who shipped production workflows at Angel Flight West and Eureka AI, combining LangGraph + RAG (Postgres/pgvector) with strong observability (LangSmith/Langfuse). Delivered large operational gains (address lookup cut from 10 minutes to 60 seconds; accuracy to 92%) and has a track record of quickly stabilizing customer-critical pipelines (Pydantic-enforced JSON for ETL) while partnering with sales/ops to drive adoption.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“Graduate researcher who builds production-grade AI systems spanning LLM security evaluation and on-device RAG. Created HoneyLearner, a self-learning attack framework using GPT-4-class models as structured black-box attackers against honeywords defenses, with rigorous metrics and reproducible orchestration (Airflow/Spark/Kafka/Docker). Also partnered with agriculture scientists at Texas A&M–Corpus Christi to deliver UAV + 3D point-cloud crop-stress maps that cut time-to-insight ~40% and enabled ~30% earlier interventions.”

“Built and deployed a production LLM-powered email assistant (“wood flow”) for a local pet resort to automate after-hours inbound email handling, including email categorization and context-aware auto-responses. Uses n8n for orchestration and applies CRISP-DM, load/edge-case testing, and RAG-based context retrieval, and has experience presenting AI solutions with budgeting and ROI to a non-technical founder.”

“Operations/people leader with strong analytics orientation who has led large multi-site teams and built KPI dashboards to drive measurable performance gains. Notably improved a high-volume intake process at NYCHHC by ~20% through real-time throughput/wait-time tracking and workflow standardization, and partnered with senior leaders at Amalgamated Bank to lift engagement by 15%.”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

Ambuk Rehani

Screened

Mid-level AI/Backend Engineer specializing in RAG and data platforms

Dallas, TX7y exp

“Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.”

Erik Arriaga

Screened

Mid-level Data Engineer specializing in cloud data pipelines and machine learning

Austin, TX4y exp

“Experience spans college-built AWS-hosted Python/Flask web apps and enterprise data work at General Motors, including PostgreSQL query optimization on millions of records and multi-tenant-style data isolation using group-based, column-level permission grants. Also built an AWS-hosted meat price prediction dashboard using Dash/Plotly and ran large nightly data pipelines orchestrated with Apache Airflow.”

Edward Fisher

Screened

Director-level Talent Operations & Recruiting Operations leader specializing in scalable systems

Dallas, TX11y exp

“Talent/Recruiting Operations leader from high-volume, customer-centric environments who has managed teams of 18–42 and owned workforce planning, QA calibration, onboarding/training, and process optimization. Notable for redesigning a service-coordinator hiring funnel (standardized competency screening + scheduling automation + revamped onboarding) driving 8–10% service-level gains and faster fills, and for leading cross-functional system implementations with strong reporting/analytics rigor.”

“AI/LLM engineer who has shipped production RAG copilots and multi-agent workflows, including a real-time Llama3 (Ollama) copilot backend handling 12k+ concurrent queries at 99.9% uptime. Deep on orchestration (Langflow/Airflow/Kubernetes), reliability evaluation (hallucination detection, p95 latency, token cost), and monitoring (Prometheus/Grafana), with demonstrated stakeholder-facing analytics delivery via Tableau.”

Aaron Rodrigues

Screened

Junior Business & Data Analyst specializing in FinTech and banking analytics

Jacksonville, FL5y exp

“Analytics professional with Travelex experience spanning SQL ETL, Python-based machine learning workflows, and Power BI dashboarding in risk, fraud, and AML contexts. Stands out for replacing a $150K+ third-party compliance tool with internal dashboards and for materially improving operational efficiency through alert tuning, cutting alert volume by 50% and false positives by 60%.”

Bryan Jones

Screened

Mid-level Data Analyst specializing in analytics, budgeting, and sports data systems

Maryland, USA5y exp

“Baseball advisor/recruiter with a player-development lens shaped by his own injury experience, combining TrackMan-driven analytics with deep coach and program relationships. He has helped athletes navigate high-stakes draft, rehab, and college decisions, including identifying under-scouted talent like John Klein and supporting his path to the Twins' 40-man roster.”

“HRIS/payroll and compensation professional with strong Workday expertise spanning end-to-end U.S. payroll, compensation cycle support, and cross-functional reconciliation with finance. Stands out for building practical controls and Excel-based audit tools that catch issues early, including resolving integration failures and uncovering a ~$25K payroll/GL variance before close.”

Almas Fathimah

Screened

Senior AI Engineer specializing in Generative AI and ML platforms

USA7y exp

“Built and owned a production RAG-based conversational AI system at Entera for real estate analysis, taking it from experimentation through AWS deployment, monitoring, and iterative improvement. Demonstrates strong practical judgment in retrieval design, LLM safety, and scalable Python service architecture, with measurable impact including 30-40% reduction in manual analysis time and roughly 30% better response accuracy.”

“Enterprise implementation/deployment specialist focused on HRMS and payroll systems across APAC customers, combining cloud/hybrid (AWS/Azure/GCP) integration work with strong client-facing delivery. Demonstrated ability to debug complex production issues across application, database, and network layers (e.g., isolating VPN/router congestion) and to tailor Python-based data cleaning/scoring/utilities to customer-specific workflows.”

TEJASWI GUDIMETLA

Screened

Mid-level Software Engineer specializing in backend systems, microservices, and AI pipelines

North Carolina, USA4y exp

“AI/LLM engineer focused on building reliable, scalable multi-agent and RAG-based pipelines across microservices. Stands out for combining practical experimentation with strong engineering discipline around schema validation, retries, observability, and structured API contracts to make LLM systems production-ready.”

“Consulting-oriented candidate with hands-on experience advising a Detroit restaurant on market expansion through B2B partnerships and new revenue channels, while also supporting a technology company's sales process improvement as a business analyst. Brings a blend of market research, persona development, client acquisition outreach, and the ability to turn analysis into practical business recommendations.”

“Front-end engineer focused on sophisticated enterprise analytics dashboards for business and operations users. They combine strong React/TypeScript architecture with deep browser-performance knowledge and practical UX improvements for demanding expert users, especially in data-heavy applications with large tables, filtering, and role-based workflows.”

“Frontend engineer with experience building data-intensive, near real-time React/TypeScript applications in credit reporting and logistics tracking. Stands out for performance-focused architecture across dashboards and map-based products, including large dataset optimization, profiling, and Mapbox visualizations with live asset tracking.”

Anirudh Raj

Screened

Entry-Level Machine Learning & Cloud Engineer specializing in AI data pipelines

Boston, MA

“Early-career cloud/appsec-focused engineer with hands-on experience building secure, observable microservice systems on AWS (IAM least privilege, KMS encryption, Secrets Manager, CloudWatch, ALB) and troubleshooting autoscaling-related 500s down to connection pooling issues. Also deployed heavy ML workloads on Kubernetes by decomposing diffusion/transformer services, using workload identity to eliminate static credentials, and maintaining GitOps-style deployment audit trails.”