Vetted Unit Testing Professionals

“Backend/data engineer with hands-on AWS serverless and data platform experience: built a Lambda-based reCAPTCHA/token validation + SES subscription workflow and designed Glue ETL pipelines producing Parquet datasets for Athena. Also led a high-traffic multilingual media CMS modernization at Radio Free Asia by migrating to Arc XP using parallel runs, phased rollouts, feature flags, and rollback plans.”

“Frontend engineer who led end-to-end development of a React-based carbon credits data collection and analysis tool, focusing on scalable architecture and performance (code splitting, SWR caching, memoization, Lighthouse monitoring). Experienced building complex route-driven TypeScript wizards with Redux/Rematch state persistence, and refactoring legacy monolithic UI into reusable, tested, documented components while shipping deadline-driven features using feature flags and tight QA collaboration.”

“Built and deployed production LLM systems for summarizing sensitive legal and financial documents, emphasizing GDPR-aligned privacy controls and scalable hybrid cloud architecture. Experienced with Kubernetes/Airflow orchestration and rigorous testing/monitoring practices, and has delivered measurable business impact (18% conversion lift) by translating AI outputs for non-technical marketing stakeholders.”

“Built and deployed a scalable, production-ready LLM knowledge assistant using a RAG architecture (LangChain + vector store/FAISS) to replace keyword search for internal documents. Demonstrates hands-on expertise in hallucination reduction and retrieval quality improvements through semantic chunking, similarity tuning, prompt design, and human-in-the-loop validation, plus strong stakeholder communication via demos and visual explanations.”

“Frontend engineer who led end-to-end delivery of a customer-facing ticket booking platform at Simplii Financial, emphasizing scalable React architecture, performance (code-splitting/lazy loading), and accessibility. Also built a data-heavy real-time metrics dashboard at Q-TED using React + TypeScript with React Query for server-state, strong API contract validation practices, and tight QA/product/design collaboration for fast, stable releases.”

“Built a production "Mini RAG Assistant" for internal document Q&A, focusing on grounded answers (anti-hallucination), retrieval quality, and latency/cost optimization. Uses LangChain/LangGraph for orchestration and applies a metrics-driven evaluation loop (including reranking and semantic chunking improvements) while collaborating closely with product stakeholders.”

“Engineer with experience in regulated healthcare and financial systems, including a United Health healthcare service migration to AWS. Built documentation-as-code for CI/CD (Jenkins/Docker/Kubernetes/Terraform + GitHub Actions) that accelerated release cycles from 3 weeks to 4 days and tied security configuration (Spring Security/OAuth2/JWT) directly to HIPAA/GDPR compliance. Strong in observability-led incident response (ELK/Prometheus/Grafana) and performance tuning (PostgreSQL, async processing), citing MTTR reduction from 3 hours to 50 minutes and support for 250K+ concurrent users.”

“AI engineer who shipped a production KYC facial liveness/recognition pipeline (10k+ monthly verifications), including an on-prem, GPU-hosted Qwen3-VL vision-language fallback to detect spoofing/replay attacks. Also helped build a deterministic multi-agent orchestration layer powering a marketplace with Solana on-chain payments, abstracting blockchain complexity behind an API, and has experience translating real-world needs from non-technical stakeholders (construction) into practical document-reading solutions.”

“Robotics software engineer focused on ROS2 autonomy stacks, with hands-on work spanning semantic 3D SLAM, sensor fusion, and controller customization. Built an indoor GPS-denied semantic SLAM system (>95% accuracy) and extended Nav2’s MPPI controller with a custom C++ critic to keep an agricultural rover centered in crop rows, boosting CO2 laser weeding effectiveness by 40%. Strong in simulation-to-real workflows (Isaac Sim, Gazebo Ignition) and deployment automation (Docker on Jetson Orin NX, GitHub Actions CI/CD).”

“Former Wipro engineer who contributed to an open-source JavaScript utility library focused on frontend validation/formatting, including adding a cross-browser date-formatting module. Experienced in OSS maintenance (bug fixes, PR reviews, docs), performance profiling/benchmarking (Chrome DevTools, Node.js performance hooks), and improving community support workflows with issue templates and diagnostic logging.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

“Full-stack engineer who built a cloud-native customer servicing platform at Synchrony using React 18/Next.js (SSR) and Spring Boot microservices on AWS. Experienced with high-volume, event-driven systems (Lambda/SNS/SQS) and strong distributed-systems rigor around data integrity (idempotency, DynamoDB conditional writes) plus production-grade security/observability (JWT/OAuth2, WAF, Actuator, Splunk).”

“Senior Data Scientist in consulting who has built production RAG systems for insurance/annuity document search at large scale (100K+ PDF pages), emphasizing grounded answers, guardrails, and low-latency retrieval. Experienced in end-to-end MLOps for LLM apps—monitoring, evaluation sets, drift handling, and safe rollouts—and in orchestrating complex pipelines with Prefect/Airflow and deploying services on Kubernetes.”

“LLM/RAG-focused engineer who repeatedly takes agentic workflows from impressive demos to dependable production using rigorous evals, SLOs, and deep observability. Has led high-impact incident mitigation (22-minute MTTR during a major sale) and developer enablement workshops, and partnered with sales to close a $410k ARR enterprise deal with a tailored RAG pilot (FastAPI/pgvector/Okta/InfoSec-ready).”

“Built a production AI pipeline at EyCrowd to automatically grade shaky outdoor user-submitted brand videos using CV + CLIP/BLIP and a LangChain RAG layer per brand, with GPT-4 generating structured JSON explanations and grades. Optimized for latency and cost (batch PyTorch inference, caching), cutting review time from ~8 minutes to <2 minutes while reaching ~90% alignment with human graders and supporting thousands of videos/day.”

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

“Backend-focused engineer with Python experience building network design/exploration tooling integrated into an existing infrastructure framework via APIs, including use of graph algorithms for routing/target discovery. Has also supported .NET to .NET Core and database migrations by strengthening test infrastructure, and improved messaging-system stability (RabbitMQ) through performance monitoring and memory leak troubleshooting.”

“Full-stack engineer who has shipped a real-time social engagement feature (live messaging + personalized feeds) for a career networking platform, owning everything from WebSockets/SSE and JWT+Redis auth through Docker/Kubernetes production deployment. Also built a production Flask backend for an AI-driven movie recommendation system on AWS, with strong API design (versioning/error standards) and hands-on performance tuning (Typesense +47% query improvement, Postgres indexing, Redis caching, CloudWatch-driven incident response).”

“QA automation-focused candidate with hands-on ownership of unit and integration test suites, including CI/CD integration in GitLab. Caught a database-query regression that would have shipped incomplete API data by relying on automated integration tests, and has practical Cypress experience stabilizing flaky tests using cy.intercept()/cy.wait() and stable selectors.”

“Backend-focused TypeScript/Node.js engineer who owned a production microservice for transactional workflows in a React + microservices platform, integrating REST and Kafka event processing. Emphasizes operability and correctness (idempotency keys, exponential backoff retries, DLQs, centralized logging/metrics/alerts) plus strong API DX via versioning and Swagger/OpenAPI with improved error contracts based on developer feedback.”

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”