Vetted Schema Design Professionals

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp

“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Early-career software engineer who owns revenue-critical invoice processing and internal ops tooling end-to-end. Has built TypeScript/React systems backed by MongoDB and Temporal, and designed scalable SQS-based onboarding workflows with FIFO/DLQ monitoring. Notably redesigned an Authzed SpiceDB authorization model, shrinking a 500+ line schema to ~20 lines while meeting sub-100ms p95 latency.”

Kaushik Sriram

Screened

Mid-level Software Engineer specializing in event-driven FinTech backend systems

San Francisco, CA5y exp

“Senior/Staff-level backend/platform engineer who owned Stripe’s global payout settlement system end-to-end, building an event-driven Python/Kafka platform processing millions of events daily across 30+ countries. Deep experience operating high-reliability distributed systems in production (incidents, replays/backfills, schema evolution, observability) and scaling on AWS/EKS with strong testing and deployment practices.”

“Candidate is a hands-on engineer using AI as a controlled coding partner rather than an autonomous decision-maker. They have practical experience designing and leading structured multi-agent coding pipelines with specialized roles for code generation, review, and test coverage, and show strong judgment around reliability through schemas, guardrails, reviewer gates, and manual validation.”

Paul Liu

Screened

Senior Software Engineer specializing in cloud-native backend and web/mobile apps

Seattle, WA8y exp

“Backend engineer with Tesla experience building Python-based, serverless microservices for a supply chain portal, including a MongoDB-backed tracking/logging system and a reconciler Lambda to manage retries and failures. Has hands-on Kubernetes (EKS) and GitOps (Argo CD) experience, plus real-time Kafka pipelines for fleet/IoT telemetry and proxy-based migrations from monolith systems to AWS databases.”

“Full-stack engineer in the investment tracking/financial reporting space who built an automated reporting dashboard and compliance/reporting pipeline end-to-end using Next.js (App Router, server/client components), REST, and Postgres. Demonstrated measurable performance wins (~30% faster loads) through caching and query optimization, and built durable orchestrated workflows in n8n with retries, idempotency, and reconciliation checks.”

Yeshwanth Sai Pala

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech

Remote, USA4y exp

“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”

“Backend owner at Apple for an AI workflow orchestration service, with hands-on experience stabilizing peak-traffic production systems using OpenTelemetry-style tracing, bounded async concurrency, and database performance tuning. Built and shipped a Python LLM-agent orchestration layer to automate multi-step operational workflows, emphasizing guardrails, auditability, and deterministic fallbacks to keep non-deterministic AI behavior production-safe.”

Asrith Velireddy

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

“Built multiple AI-powered products from scratch, including ConnectAbility, an accessibility tool combining computer vision and LLMs to describe visual content for users with disabilities, and SpamBack!, a macOS app that detects scam texts and auto-generates responses. Stands out for full-stack/backend ownership of applied AI systems, especially around async workflows, inference performance, and reliability safeguards.”

Robert Davis

Screened

Staff Software Engineer specializing in backend and distributed systems

Remote, US19y exp

“Backend engineer who co-launched SkyKick’s Office 365 SharePoint/Exchange backup product, built the MVP, and then architected and led its design for 9 years. Stands out for high-scale systems expertise, including an algorithmic redesign that cut cloud costs by an order of magnitude, plus earlier experience integrating speech recognition systems in noisy real-world customer environments.”