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Vetted Rate Limiting Professionals
Pre-screened and vetted.
Executive FinTech Engineering Leader specializing in core banking, payments infrastructure, and AI
Tirth Shah
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”
Samuel Braude
Junior Computer Science student specializing in robotics, ML, and quantum computing research
“Hands-on engineer who has taken an LSTM Bitcoin forecasting model from notebook to a production-grade, monitored API (Docker/Gunicorn/Nginx, Prometheus/Grafana, blue-green rollback) delivering 99.9% availability and ~110–120ms p95 latency. Also built an RFID self-checkout prototype spanning Raspberry Pi + firmware + networking, using deep instrumentation to eliminate double-charges/timeouts (<0.1%) and reduce checkout time ~20% through idempotency, debounce logic, and hardware fixes.”
David Pang
Intern Full-Stack Software Engineer specializing in web apps and healthcare APIs
“Full-stack developer who built an end-to-end e-commerce application with admin/blog/announcement features using Node/Express and AWS S3, emphasizing security via expiring presigned URLs. Also has strong distributed-systems fundamentals from implementing the Raft consensus algorithm (replication logs, majority acks, leader elections) and has created build automation tools (GNU Makefiles/scripts) to streamline team workflows.”
Shreyas Krishnareddy
Junior AI/Software Engineer specializing in NLP, RAG, and resume parsing
“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”
Jorge Martinez-Rocha
Junior Software Engineer specializing in automation and full-stack development
“Backend-focused engineer who built a time-sensitive data retrieval system for a source with no public API, using an AWS EC2-hosted persistent browser session plus a PostgreSQL TTL caching layer—cutting manual retrieval by 99% and achieving sub-10-second average retrieval. Emphasizes production security (Secrets Manager, encryption, IP allowlisting, rate limiting) and robustness via testing and edge-case handling (atomic file operations).”
Vaibhavi Madhav Deshpande
Mid-level AI Engineer specializing in LLM agents, RAG, and data pipelines
“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”
Haneesh Kapa
Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems
“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”
HarshelSrivatsava Srivatsava
Intern Full-Stack Engineer specializing in AI-powered SaaS products
“Solo builder of OGym, shipping production AI features for gyms that turn member behavior/health data (workouts, attendance, nutrition, payments, device metrics) into prioritized, actionable owner and member insights. Designed and implemented FastAPI backends, PostgreSQL-based RAG workflows, guardrails (RBAC/validation/rate limiting), and real-user evaluation loops, with a strong focus on latency/cost optimization and reliable data pipelines.”
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”
sandeepreddy bimidi
Junior Full-Stack Software Engineer specializing in scalable web services
“Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.”
Citlalli Trejo
Entry-level Full-Stack Developer specializing in web, iOS, and ML applications
“Builder-minded software engineer who has shipped a live iOS budgeting app and a solo full-stack web app, with strong evidence of production discipline in both ML and fintech-style integrations. Particularly compelling for roles involving ambiguous product problems, third-party API reliability, and end-to-end ownership from backend logic through user-facing recovery flows.”
Maheswari Thokala
Mid-level Full-Stack Engineer specializing in cloud microservices and REST APIs
“Backend engineer building an AI-powered social media platform on AWS, with hands-on experience shipping LLM-backed application features and improving production performance under high traffic. Strong focus on reliability/observability (CloudWatch, structured logs, health checks) and database optimization (MongoDB explain/slow logs, indexing, caching, connection pooling).”
Mid-Level Full-Stack Software Engineer specializing in cloud-native security & compliance platforms
Intern Backend Developer specializing in AWS serverless and distributed systems
Junior Full-Stack Software Engineer specializing in web applications and Healthcare IT
Mid-level AI/ML Engineer specializing in LLM automation and data ingestion systems
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Mid-level Software Engineer specializing in full-stack web and cloud systems
Junior AI Engineer specializing in production RAG systems and GPU-accelerated inference
Jaiden Kettleson
Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps
“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”