Vetted Amazon EC2 Professionals

“Software engineer/intern who built an EV charging station management platform from scratch (TypeScript/Next.js/Node/Express/Postgres) with real-time OCPP WebSocket operations and payment processing, iterating quickly based on operator feedback. Also created an internal CloudWatch log aggregation dashboard with Slack alerts that was adopted team-wide, addressing API rate limits and log-format inconsistencies through caching, pagination, and standardized parsing.”

“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”

“Frontend engineer with React/TypeScript experience who has led end-to-end UI work on a fintech product (React frontend with NestJS APIs), emphasizing performance (virtualized rendering, memoization, lazy loading, profiling) and quality at scale (unit tests and TDD). Also built and iterated on a snooker sports app, simplifying UX through audience-focused design decisions and streamlined onboarding.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“Built and deployed ForensicLLM, a local domain-specific LLaMA-3.1-8B model for digital forensic investigators using RAFT + RAG over 1000+ curated research papers, with citation-aware responses and rigorous evaluation (BERTScore/G-Eval). Deployed via vLLM and Docker and validated through a chatbot survey with 80+ participants; published at DFRWS EU 2025.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Game QA professional with AAA open-world shipping experience, owning high-impact quality risks like save-data corruption, progression blockers, performance drops, and multiplayer desync. Demonstrates strong systems thinking and exploit prevention (e.g., reproduced and helped fix a reward-duplication race condition using network interruption + rapid input), with disciplined JIRA/TestRail workflows and evidence-driven bug reporting.”

“Cloud/DevOps-focused engineer with hands-on experience building Azure DevOps CI/CD pipelines for containerized applications deployed to AKS, including security scanning, approvals, versioned artifacts, and rollback. Also implemented Terraform-based IaC for Azure (VNets/subnets/NSGs/AKS) with modular design, remote state/locking, and drift detection; resolved a real deployment outage caused by an Azure RBAC permission change.”

“Software engineer with experience across edtech, live gaming, and an AI document intelligence platform, delivering end-to-end customer-facing features and production backends. Built secure, automated live-session scheduling integrating Zoom and TalentLMS (JWT/RBAC, idempotency, transactions) cutting setup time from ~3 minutes to under 1 minute, and optimized real-time gaming dashboards/APIs with query tuning, caching, and CDN improvements (~60% latency reduction under peak load) on AWS.”

“Business/partnerships-focused project/program leader with government contracting (SLED) experience who supported a cybersecurity solicitation response for Amtrak after a breach, using NIST-aligned posture assessment and recommending MFA, Zero Trust, and incident/vulnerability registers. Also led an urgent COVID-era recovery from a server crash by coordinating a move to AWS EC2 and managing vendor negotiation and project timelines for a CRM platform serving the winery/beverage industry.”

“Backend-leaning full-stack engineer in fintech/payments who shipped an end-to-end Stripe payments + webhook system for a financial microservices platform, emphasizing ledger accuracy via idempotency, transactional writes, retries, and DLQs. Also delivered a real-time React/TypeScript payment status dashboard informed by user interviews, and improved production performance by 35% p95 latency through PostgreSQL tuning and Redis caching on AWS.”

“Product-minded engineer who has owned features end-to-end, including a full onboarding redesign that lifted completion ~25% and a production LLM/RAG report-generation system with strong guardrails (schema-constrained JSON, confidence gating, logging) and an automated eval/regression loop built from real user queries. Also built a scalable research data pipeline ingesting messy PDFs/JSON/CSVs with normalization, idempotent reruns, observability, and cost/latency tradeoffs.”

“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”

“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/data engineer focused on production AWS systems: builds multi-tenant FastAPI services on ECS behind API Gateway/ALB with serverless orchestration (Lambda, SQS, Step Functions) and strong reliability practices (JWT/JWKS auth, idempotency, backoff retries, structured logging). Also delivers AWS Glue/PySpark ETL pipelines with schema/data-quality controls and has modernized legacy analytics logic into Python with parity validation; improved a key dashboard SQL query from ~12–25s to ~2–3s.”

“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.”

“Unity developer focused on VR/AR multiplayer, with hands-on experience building Photon Fusion/PUN systems including networking, sync, and matchmaking. Has scaled a modular multiplayer architecture to handle more players/features while addressing latency and data consistency, and has optimized Meta Quest/mobile performance using pooling, LODs, batching, and profiling.”

“Unity lead developer who built a turn-based soccer game (Soccer Glory/Score Hero-like) with complex in-air ball simulation, AI reactions, and animation handling, then scaled production by creating an in-editor level/animation authoring tool that saves to JSON and shifted level setup from devs to designers. Strong multiplayer background across Photon PUN, Mirror, UNet, and Socket.IO, including a 100-user metaverse room and practical approaches to authoritative simulation and VR/mobile performance optimization (Meta Quest 90 FPS).”

“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.”

“Frontend/fullstack lead who inherited a messy psychological app with production issues, drove a rapid stabilization (2–3 weeks) and major performance/architecture overhaul (Redux Toolkit, memoization, caching, lazy loading, CDN offload to S3/CloudFront). Also owns delivery and infrastructure practices (multi-env, Docker, GitHub Actions CI/CD, AWS ECS + load balancing) and led a 1-week POC for an AI-powered trucking management system (app.neblo.ai).”

“Frontend engineer with experience building an orthodontics-focused IoT web product, including a complex React + TypeScript 3D treatment viewer with a layered architecture (data layer plus multiple canvas asset managers). Has applied performance techniques like caching and Web Workers to keep the UI responsive under heavy computation, and shipped monetization features such as payments and coupon/discount flows with structured QA rollout.”

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

“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.”