Vetted Data Validation Professionals

Ridwan Oladipupo

Screened ReferencesStrong rec.

Senior Frontend Engineer specializing in React, Node.js, and AWS

Halifax, Canada11y exp

“Frontend engineer who led the Dash Merchant App and Backoffice platform (React/TypeScript/Redux) supporting $50M+ in monthly transactions. Focused on scalable architecture and reliability—introduced typed API layers, centralized error handling, and performance optimizations (including ~40% load-time reduction) while driving team adoption through incremental refactors, templates, and pairing.”

“Product builder with an operations leadership background who created PMTutorPlus, an AI-enabled project management education platform centered on guided learning rather than answer-giving automation. Stands out for thoughtful human-centered AI judgment, strong workflow/UX structuring for complex products, and a player-coach style that combines product strategy, systems thinking, and execution.”

“Analytics-focused candidate with hands-on project experience in SQL data preparation and Python-based churn modeling. They demonstrated a practical approach to turning messy multi-source data into reporting tables, validating data quality rigorously, and translating churn insights into targeted retention strategies.”

Priyank Jhaveri

Screened ReferencesModerate rec.

Junior AI/ML & Mobile Engineer specializing in LLMs, synthetic data, and React Native

New York, United States1y exp

“Currently at Uplift AI shipping production LLM features that generate personalized growth insights from user reflections using BERT + embeddings + RAG, with strong safety/guardrail practices for sensitive contexts. Also built an end-to-end React Native UGC challenge submission/moderation system that improved repeat submissions and 7-day retention, and has applied rigorous clinical-style evaluation methods on a dental X-ray disease detection project to reduce false negatives.”

“Analytics professional with operations-grounded experience at WWEX Group who built a Snowflake/dbt fleet-efficiency data model combining telematics, ERP, and driver logs into near real-time executive reporting. They pair strong SQL/Python workflow automation with practical stakeholder enablement, and cite measurable impact including cutting reporting time from 72 hours to 15 minutes and helping drive $450K in quarterly fuel savings.”

Mudit Devenkumar Choksi

Screened ReferencesModerate rec.

Junior Full-Stack Software Engineer specializing in web apps, automation, and cloud systems

Louisville, KY2y exp

“Engineer with hands-on experience owning end-to-end industrial automation deployments and real-time data systems. Most notably led a multi-million dollar warehouse automation implementation that reduced manual intervention by 25%, while also building streaming text analytics pipelines and strengthening production reliability through robust observability and pipeline controls.”

“Backend engineer with deep Kafka/FastAPI microservices experience who redesigned a notification pipeline to cut end-to-end latency from ~5s to ~3s (including custom partition assignment and consumer tuning). Led a high-stakes ClickUp-to-Oracle migration of 1M+ records using idempotent ETL, reconciliation, and shadow deployment to achieve >99% integrity with zero downtime, and has hands-on production security implementation with Django/DRF (JWT + RBAC).”

“Backend engineer who rebuilt and refactored high-traffic systems at Phenom using Java/Spring Boot/Play and also designs Python/FastAPI services. Focused on measurable reliability and performance gains through DB/query optimization, async processing, and strong observability, with disciplined rollout practices (feature flags, parallel runs, rollback) and security patterns including token auth and row-level security.”

“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”

“Data/ML practitioner who has built a production RAG-based knowledge assistant integrated into Microsoft 365/internal dashboards to help employees query internal documents in plain English. Experienced orchestrating and hardening ETL pipelines with Airflow and Azure Data Factory (validation, retries, monitoring) and running end-to-end model evaluation and production performance tracking via Power BI.”

“Enterprise Customer Success/implementation leader in fintech (Optimus) specializing in payment reconciliation platforms, complex integrations (API/SFTP), and data normalization across processors/banks/ERPs. Demonstrated measurable impact (60–70% reduction in manual reconciliation) and strong cross-functional/product influence, including roadmap improvements for exception management and successful land-and-expand into fee management.”

“Data/ML platform engineer with ~6 years in financial services and enterprise data platforms, building regulated fraud/credit-risk pipelines on AWS (Airflow, EMR/Spark, MLflow) and an Azure lakehouse ingesting 50+ sources and serving ~100M records/day. Also led an early-stage deployment of a RAG-based internal AI search tool using AWS Bedrock and LangChain with automated evaluation to validate LLM accuracy.”

Gautam Agrawal

Screened

Mid-Level Software Engineer specializing in backend systems, cloud, and applied LLM/NLP

IN, USA4y exp

“Applied LLMs to classify long nonprofit mission statements into 8 segments without labeled data, using an ensemble of clustering/embedding methods plus zero-shot RoBERTa/BART and a Tree-of-Thought prompting pipeline with LLM-as-judge evaluation (Gemma). Also built LangChain/LlamaIndex agentic RAG workflows including a text-to-SQL data analysis assistant grounded on DB schema with retries and performance optimizations on an HPC cluster.”

Leon Hsi

Screened

Mid-level Solutions Consultant / Full-Stack Developer specializing in APIs, SQL, and cloud systems

Houston, TX4y exp

“Builder with hands-on security hygiene experience from developing a helpdesk portal handling sensitive payment/invoice data, focusing on RBAC, least-privilege integrations (QuickBooks/Atera), and tightening API authorization to prevent cross-account access. Also built personal projects integrating Twilio/Callkeep/Supabase/OpenAI with strong key management and defensive handling of real-world API/network failure modes; holds an ISC2 certification and is actively deepening cloud security skills.”

Vengalarao Pachava

Screened

Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems

Irving, TX2y exp

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

“Analytics professional with experience across Dreamline AI, Ultron Technologies, and Infolabz, building SQL/Python data pipelines and BI dashboards for incentive, FMCG, and retail use cases. Stands out for turning messy multi-source data into trusted reporting, automating recurring analytics, and tying dashboard adoption to measurable business outcomes like 50% faster reporting and 30% ROI improvement.”

“Built a production ad-spend optimization system that combined deterministic audit logic with LLM-generated explanations, surfacing severe inefficiencies including 70-90% wasted spend in some Google Ads accounts. Stands out for pairing measurable business impact with pragmatic AI safety and usability decisions, including approval-gated execution and structured, human-readable recommendations.”

“Built GynAI, an end-to-end maternal clinical decision support platform for OB/GYN practices and hospitals in North America, combining predictive ML with RAG-based LLM explainability. The candidate emphasizes real production ownership across experimentation, deployment, monitoring, and iteration, with reported impact including fewer delayed interventions in high-risk pregnancies and a 15-20% reduction in false positives.”

“Production-focused ML/AI engineer with hands-on ownership across classical ML and GenAI systems, from CV/NLP services to enterprise RAG. Stands out for combining research-to-production execution with measurable business impact: 40% processing-efficiency gains, 35% fewer support tickets, 5x latency improvement, and 3x throughput gains while maintaining safety and quality.”

“Built Python-based data workflow integrations for a Huntsman Cancer Institute research project, focusing on reliable upload, validation, processing, and retrieval of messy research data. Demonstrates strong practical instincts around automation hardening, observability, and translating ambiguous manual processes into structured workflows, including Selenium automation when APIs were unavailable.”

“Frontend-leaning full-stack engineer who has rebuilt a high-volume order management system in Next.js/TypeScript for 6000+ active orders and also owned end-to-end product/data architecture in Firebase/Firestore. Stands out for strong performance instincts, type-safe frontend architecture, and pragmatic 0→1 execution across UI, APIs, and data pipelines.”

Venkatasai Vorsu

Screened

Mid-level Full-Stack Software Engineer specializing in Java microservices and cloud platforms

Houston, TX5y exp

“Full-stack engineer with strong React/TypeScript and Spring Boot experience in banking and financial systems, focused on real-time transaction monitoring and payment tracking products. Stands out for scaling high-volume dashboards, solving rendering bottlenecks in live data UIs, and owning features end-to-end from frontend through APIs, Oracle data layer, cloud deployment, and production monitoring.”

“Salesforce admin/developer with 3-4 years of experience spanning admin fundamentals through Apex, LWC, integrations, and Experience Cloud. Particularly strong in Service Cloud and case management optimization, including a redesign that cut manual effort by 50-60% while improving reporting accuracy and response times.”

Siddharth Ranjan

Screened

Mid AI/ML Engineer specializing in LLMs, RAG, and cloud AI systems

Maryland, USA4y exp

“Built an AI-powered job matching platform end to end using AWS, Gemini, FastAPI, TypeScript, embeddings, and vector search. The standout result was automating manual matching workflows and scaling resume processing to roughly 2,000 resumes per minute while monitoring quality with F1 score and latency metrics.”