Vetted Visual Studio Code Professionals

“Built a lightweight internal JavaScript analytics tracker capturing user interactions (clicks, page views, custom events) with debounced batching, automatic session tracking, and offline event caching via a localStorage-backed append-only queue. Demonstrates practical performance optimization mindset (profiling, memoization/caching) and React performance tuning.”

“Early-career developer whose experience comes from classroom projects but has completed full end-to-end web app delivery: implemented login/search/favorites, integrated external APIs, and deployed to Google Cloud for multi-device use. Demonstrates user-centered iteration by recruiting friends to test and provide feedback, and has hands-on production-style debugging experience (e.g., resolving CORS issues during deployment).”

“Founding engineer for an AI product (“world’s first funny AI”) who designed and implemented the full-stack architecture (React/TypeScript + Node) and migrated production from Vercel to AWS. Shipped a Lambda-based image pipeline that eliminated lag/missing images and brought page load times to under a second, and has hands-on experience integrating multiple LLM providers (OpenAI, Claude, Gemini, Grok) with structured-output and self-check reliability techniques.”

“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”

“Software engineer in the mortgage/financial services domain (Freddie Mac) who builds end-to-end loan origination and credit risk capabilities using Spring Boot microservices, Angular dashboards, and data pipelines. Delivered measurable impact (30% reduction in underwriting turnaround time) and emphasizes production reliability/compliance with strong guardrails, observability, and evaluation loops for risk scoring systems.”

“Uber engineer who has owned internal products end-to-end across backend (Spring Boot microservices, MySQL) and frontend (React), including performance optimization and secure JWT-based auth. Also shipped a production internal RAG/embeddings LLM support assistant over policy docs and support tickets, with guardrails (confidence thresholds, human review) and an evaluation loop that directly reduced hallucinations.”

“Software engineer with hands-on embedded/robotics coursework experience (Arduino sensor integration and input handling built from scratch without external libraries) and strong DevOps/engineering productivity impact at work, including leading a CI/CD enhancement that runs only impacted tests to catch issues before PR approval.”

“Robotics-focused candidate with multiple university robotics projects (MTE 380, MTE 544) and ROS 2 (Humble/Galactic) experience spanning perception, navigation, and simulation. Built a vision-based line-following and retrieval robot using HSV filtering and homography, and debugged real-time PID overshoot issues via timestamping/rate-limiting. Comfortable with distributed ROS 2 architectures (Python perception + C++ control), DDS/QoS, Gazebo testing, and Dockerized deployment.”

“Backend engineer with hands-on experience scaling a Python/Flask incident-logging platform processing thousands of daily logs. Strong in performance tuning (PostgreSQL/SQLAlchemy query optimization, partitioning, summary tables) and reliability patterns (Redis caching, Celery background workers, Docker + Jenkins CI/CD), with some multi-tenant data isolation experience via separate DBs/schemas.”

“Consulting-background AI practitioner who led a production LLM pipeline on Snowflake Cortex to map hundreds of thousands of messy OCR/form-based contract fields into standardized Salesforce fields, including confidence scoring and an LLM-driven feedback loop. Strong focus on real-world constraints—token limits, cost control, and evaluation without ground truth—paired with frequent stakeholder-facing progress reporting.”

“Backend engineer at Goldman Sachs who deployed internal LLM-powered utilities to summarize operational logs/tickets, with a strong emphasis on data sensitivity and reliability. Built deterministic workflows with template-based prompts, confidence checks, and rule-based fallbacks, and used monitoring plus failure-rate metrics to tune performance; also has hands-on Temporal orchestration experience for resilient async backend jobs.”

“Software engineer with hands-on experience improving performance and reliability in financial workflows (settlements/loan processing), spanning React/TypeScript and Angular frontends plus Spring Boot microservices. Has delivered measurable latency improvements using PostgreSQL optimization and Redis caching, and has operated Kafka-based systems at scale with idempotent processing and backoff/retry strategies while iterating internal ops tooling with support/finance teams.”

“Backend/infra engineer with experience across Nestle, J.P. Morgan, and Capgemini, combining ML systems work (YOLOv8/PyTorch object detection with TFLite edge deployment) with production-grade cloud/Kubernetes operations. Has delivered measurable impact via AWS migrations (25% cost reduction, 99.9% availability), microservice modernization (35% faster processing), and low-latency Kafka streaming for financial dashboards (<100ms) using DLQs and idempotent consumers.”

“Software engineer working on an e-commerce platform, currently building a RAG-based recommendation system with a team new to the technology. Has delivered an end-to-end React/TypeScript website for a local car dealer and built an internal "encryption as a service" tool to secure sensitive data across repositories and through release/UAT, with experience debugging microservices integration issues.”

“AI/ML engineer with production experience building LLM-powered document intelligence and customer support systems in healthcare/insurance, emphasizing high-accuracy RAG, long-document processing, and robust monitoring/fallback mechanisms. Also automates and scales ML lifecycle workflows using Apache Airflow and Kubeflow, and partners closely with non-technical operations stakeholders to drive adoption.”

“Software engineer with experience building a Vue.js/TypeScript internal component library (with Jest testing standards) and improving JS runtime performance via profiling, code splitting, and lazy loading. Also led documentation and community support for a Python ML utility library, diagnosing metric-calculation bugs for imbalanced datasets and driving large reductions in support inquiries through targeted docs, tests, and rapid hotfixes in a startup environment.”

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“QA professional with Apple experience supporting the macOS Sonoma release, owning daily monitoring of submissions, crashes, regressions, and release verification across multiple OS/device builds. Notably isolated a memory-related issue in the Safari suite that caused an 18-hour build backlog and 156+ test crashes, leading to a Python-script fix that restored build times to ~1 hour and helped keep the release on schedule.”

“Full-stack engineer with production ownership across React/TypeScript, Node/Express, and Postgres, including zero-downtime releases and rollbackable migrations. Demonstrated measurable performance wins (20% response-time reduction) through DB query profiling and batching, plus hands-on AWS operations (ECS/Lambda/CloudWatch) and reliability patterns for ETL (retries, DLQs, idempotency). Experience shipping microservices quickly in ambiguous, fast-paced environments (Deloitte).”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“UE5 UI engineer who built a production-ready, modular training/procedure framework with editor tooling so curriculum designers and technical trainers could author content without Unreal expertise. Deep hands-on ownership across C++/Slate/UMG/CommonUI, including MVVM architecture, cross-platform input/navigation, and rigorous UI profiling/debug tooling that can be stripped from shipping builds.”

“Robotics/software engineer who deployed a Raspberry Pi computer-vision-controlled 3-axis platform at FuelCell Energy, improving screen-print alignment from >10mm to 0.01mm repeatability via encoder-based closed-loop PID control. Currently designing an AI-enabled hearing-aid accessory for the cocktail party problem using a multi-mic array, beamforming, and an edge-accelerated neural network with <10ms latency via shared-memory DSP integration.”