Vetted Data Validation Professionals

“Full-stack engineer who built and owned a production workflow/kanban-style drag-and-drop system in Next.js (App Router) with Postgres/Prisma, including reusable component abstractions, Cypress E2E coverage, and post-launch performance/bug ownership. Notable for measurable impact (25% faster UI dev, ~30% query perf improvement) and for leading an incremental Express→NestJS migration that reduced technical debt (~40%) through better structure, docs, and team enablement.”

“Data engineer with experience at Morgan Stanley and Star Health owning production-grade lakehouse pipelines for credit risk and healthcare datasets. Built Azure/Databricks/Delta/Snowflake-based platforms processing millions of records per day with strong data quality, observability (Monte Carlo/Azure Monitor), and reliability practices, plus experience delivering curated data services with performance tuning and backward-compatible versioning.”

“Full-stack engineer with deep startup experience building products from scratch under ambiguous requirements. Delivered a scalable, admin-configurable notification platform (Spring Boot/Java/Kafka) supporting 50+ notification types across 3 channels for 10k+ users, cutting new notification setup to ~5 minutes. Also built a Tinder-meets-LinkedIn job-swiping app (React/TS + Node/Prisma) and has hands-on AWS production ops (ECS/EKS, RDS, CloudWatch) plus multiple third-party integrations (Stripe, QuickBooks, Twilio).”

“Backend-focused engineer who built a Python-based automation system leveraging Gemini AI and prompt-driven PDF field extraction to replace a previously manual third-party workflow. Drove stakeholder alignment around accuracy/acceptance thresholds and added production-minded safeguards like graceful failure handling and backup model contingencies.”

“New grad who shipped an LLM-powered writing app (“Write-it”) to production on Azure with CI/CD (GitHub Actions + JFrog) and implemented an unconventional RAG pipeline to prevent repetitive prompts using embeddings and cosine similarity. Also participated in a Luma AI image/video generation hackathon, iterating with artist feedback and improving usability by rewriting non-technical prompts via an LLM.”

“Owned and stabilized Decathlon e-commerce payment services, taking a prototype reliability effort to production by implementing failure detection/retries, load testing, and DB performance optimizations—reducing payment failures and cart abandonment. Also demonstrates an LLM/agentic workflow support mindset with strong observability, rapid incident diagnosis, and durable prevention via RCA, safeguards, and regression/replay testing, plus experience supporting sales/support with technical reassurance.”

“Software engineer with hands-on production experience deploying Spring Boot services to AWS using Docker and Jenkins CI/CD, focused on stable releases, easy rollback, and performance improvements through monitoring/logging and query optimization. Has proven cross-layer troubleshooting skills (identified packet loss causing intermittent timeouts via network traces) and experience collaborating on-site with operators in industrial/IoT-style environments, including working alongside robotics/PLC teams.”

“Backend/data engineer who built an end-to-end real-time stock analytics platform: ingesting multi-source market data via Kafka/APIs, transforming it into dashboard metrics (e.g., Bollinger Bands), and storing in BigQuery/MySQL. Strong DevOps/GitOps experience deploying Python/Node microservices on Kubernetes with Docker/Helm, CI/CD (GitHub Actions/Jenkins), and ArgoCD, plus hands-on troubleshooting and migration work.”

“AI Researcher with production experience building a real-time computer-vision detection pipeline augmented by an LLM-based verification layer to cut false positives (~78%) and reach ~90% real-world accuracy. Also partners cross-functionally with Product/Sales/Marketing to shape AI feature prioritization and market positioning using analysis and interactive dashboards.”

“Built and owned an end-to-end real-time IoT telemetry backend that powers a digital twin experience on a Meta Quest headset, integrating Cisco LoRaWAN sensors and external REST data sources. Migrated from Azure Functions to a FastAPI service to overcome firewall constraints, add caching/fallback reliability, and significantly reduce operating cost while improving performance and evolvability.”

“Backend engineer (Encore) who led the evaluation and redesign of a high-volume, low-latency real-time retrieval/ranking and inference platform on AWS, shifting from tightly coupled services to a modular architecture for better fault isolation and independent scaling. Strong focus on production reliability, observability, and security (JWT/RBAC, multi-tenant scoping, Postgres/Supabase RLS), with disciplined migration playbooks (feature flags, shadow traffic, dual writes/reconciliation).”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built a Meta-style AI smart glasses system emphasizing on-device privacy and low-latency processing, spanning ESP32-S3/FreeRTOS firmware through an NVIDIA Jetson Linux edge-AI pipeline in Python/Docker. Strong in real-time streaming optimization (zero-copy GDMA, deterministic scheduling), encrypted transmission (AES-256), and reliability via stress testing and robust error handling; currently building CI/CD automation tests using Playwright and computer vision.”

“Built and deployed a production-grade LLM agent for credit management and accounts receivable automation, integrating ERP/MySQL data via a RAG pipeline and exposing services through FastAPI with Pydantic-validated outputs on AWS Bedrock. Emphasizes reliability and compliance for financial operations using schema validation and human-in-the-loop review, reporting ~32% reduction in manual work and ~41% improvement in response time/reliability.”

“Junior frontend developer building and shipping personal projects, including an end-to-end access control/registration app with modular/layered architecture, database modeling for scalability, and a React+TypeScript dashboard with filters and detail views. Emphasizes code quality through validation, unit tests, and modern tooling (Jest/RTL, ESLint, Zod, Postman), and uses Figma prototypes to translate UX into responsive UI.”

“Frontend-leaning full-stack engineer who owned end-to-end delivery for a high-traffic e-commerce web app (millions of monthly users), spanning UI enhancements, performance work, and third-party recommendation API integrations. Built a NestJS microservice integrated with Shopify to tag products (e.g., low stock/sale) and used queuing to handle high-volume order processing, plus created an internal CI/GitHub tool to surface test failures quickly.”

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

“Backend/DevOps-leaning engineer who has owned a Python serverless platform on AWS (Lambda, DynamoDB, Step Functions), including complex multi-step business workflows with transaction-based consistency and robust failure handling. Also supported an on-prem SQL to Azure Data Lake migration by building and monitoring Python + Azure Data Factory ETL pipelines, and led GitOps-style CI/CD automation with GitHub Actions (tests, security scans, automated deployments).”

“Full-stack engineer with strong production ownership across frontend, backend, and cloud ops. Led an AdTech “Status Centre” initiative at Beautiful Code that automated DSP integrations (XML/SOAP/REST) and reduced manual work by 90%+, using an event-driven architecture with retries and OTEL/Datadog observability. Also built and hosted a MERN app (Onegrad), load-tested with k6, and has experience running large migrations (~8k users) with validation and alerting.”

“PhD in computer engineering who has built production-oriented ML/NLP systems for space-weather prediction using Spark-based ETL on noisy satellite sensor logs. Strong in entity resolution and semantic search—fine-tuned E5 embeddings with contrastive learning and deployed to Pinecone, improving top-5 retrieval precision by 25%—and emphasizes scalable, observable pipelines with Airflow, Docker, and CI/CD.”

“Built and owned Python backend APIs for real-time educational dashboards at ASU’s Machine Learning lab, improving responsiveness by cutting latency ~35% through caching, batching, and profiling-driven optimizations. Has hands-on experience containerizing Node.js/Python services and running GitOps-style CI/CD with GitHub Actions, plus supporting smaller infrastructure transitions with reproducible, portable configs.”

“Software engineering capstone contributor who helped kickstart an ambiguous, real-client project ("The Virtual Cowboy") by designing the initial relational database and CRUD foundation, then prioritized and shipped simulation/event functionality that satisfied the client. Also building a personal full-stack Book Notes web app (Node/Express/Postgres) with Google OAuth via Passport and a books API ingestion flow focused on data quality and reliability; has deployment experience with Heroku and PythonAnywhere and is ramping up on AWS.”