Vetted Amazon RDS Professionals

Parth Shroff

Screened

Entry-level Full-Stack Software Developer specializing in AI and cloud applications

Remote, USA1y exp

“Full-stack engineer who has independently owned and shipped educational web products from concept through launch, including an AI-powered thermodynamics tutoring app and a physics learning platform tied to research publication. Strong in React plus AWS serverless architecture, with a clear pattern of working directly with non-technical stakeholders, iterating from user feedback, and maintaining production systems end-to-end.”

“Backend-leaning fintech engineer who led a small pre-seed team building cross-border remittance and payment API products. Brings a mix of technical leadership, customer-facing product discovery, and hands-on experience with scalable payment infrastructure, compliance integrations, and cloud deployment on AWS.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“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.”

“Built and productionized an LLM-powered service desk ticket triage and reporting agent that classifies, prioritizes (including sentiment/urgency), and summarizes tickets into structured SQL outputs feeding Power BI dashboards. Emphasizes production reliability (99% uptime) with retries, schema validation, confidence thresholds, human review queues, and rule-based fallbacks, delivering 85–90% reduction in manual effort and 25–30% faster resolution times.”

“Data engineer focused on building reliable, production-grade pipelines and data services end-to-end, including a 50+ GB/day pipeline ingesting from APIs/files into Snowflake with PySpark/SQL transformations. Emphasizes strong data quality controls, monitoring/retries, and performance optimization, and has also shipped a Python data API with caching and backward-compatible versioning.”

Elena Cherpakova

Screened

Junior Full-Stack Software Developer specializing in React, Node.js, and AWS

Ottawa, Canada3y exp

“Frontend engineer at WITT who led multiple end-to-end React/TypeScript products in fintech/e-commerce contexts, including a shopping cart with Stripe payments and a multi-step registration flow. Emphasizes scalable component architecture, strong QA (tests/reviews/linting), and performance work (lazy loading/memoization), plus disciplined rollout via feature flags and close product/design collaboration.”

HarshelSrivatsava Srivatsava

Screened

Intern Full-Stack Engineer specializing in AI-powered SaaS products

Birmingham, AL1y exp

“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.”

Purav Patel

Screened

Entry-level Full-Stack Software Engineer specializing in .NET, React, and cloud systems

Houston, TX1y exp

“Full-stack developer with .NET and cloud-based development experience who has built a practical AI-assisted engineering workflow using Copilot, Claude, and GPT across coding, debugging, testing, and review. Stands out for using AI to accelerate delivery while still applying careful validation and human judgment for high-impact performance and security decisions.”

“Software engineer building AI-powered automation features in commercial real estate, including brochure generation and property listing workflows. They combine FastAPI/Redis/Celery backend architecture with multi-agent LLM design, structured prompting, testing, and production monitoring, and are now actively learning RAG and vector databases to make outputs more personalized.”

“Frontend-focused developer who built Butterfly Booking, a live two-sided salon scheduling platform for both business owners and customers. Demonstrated strong ownership across UI architecture, AWS deployment, responsive design, and a mid-project database migration, with results including a successful replacement of Vagaro, over $3,000 in annual savings for the client, and roughly 20% growth in bookings after launch.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Backend/data engineer focused on climate/emissions data platforms, building production Python (FastAPI) microservices and AWS serverless/ETL pipelines (Glue/Athena/Lambda/EventBridge). Demonstrated strong reliability and observability practices plus measurable optimization wins, including cutting PostgreSQL query runtimes from minutes to seconds and reducing AWS costs from ~$6k/month to ~$400/month.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”