Vetted AWS Lambda Professionals

“Software developer with EdPlus experience building pragmatic internal tools (Next.js/TypeScript) and cloud automations (AWS) that feed Google Sheets and Looker Studio dashboards for operational visibility across 30+ student workers. Also worked on an IEEE Xplore-published missing-person tracker using a SPARQL graph database on Azure with a TypeScript/React frontend, and has experience navigating legacy access constraints (OpenVPN) while enabling remote VR previews via ngrok.”

“Backend-leaning full-stack engineer who builds and operates Spring Boot microservices with React/TypeScript frontends, using Kafka/RabbitMQ for event-driven workflows. Created an internal ops dashboard for Support/SRE with tracing, alert correlation, and self-serve actions, improving MTTR and reducing escalations while maintaining regulatory-grade reliability and security.”

“PhD researcher (University of Utah) who built a production RAG-powered Virtual Reality Research Assistant to answer lab research questions with concrete citations. Implemented an end-to-end LangChain pipeline using PyPDFLoader, chunking strategies, OpenAI embeddings, and ChromaDB, with emphasis on grounding to reduce hallucinations and ensure research-grade accuracy. Collaborated closely with a non-technical PhD advisor to scope requirements, manage cost constraints, and demo iterative progress.”

“Backend/data engineer who built an enterprise LLM assistant (AI Genie) at Broadband Insights using a LangChain + GPT-4 + Pinecone RAG pipeline to automate broadband analytics reporting. Developed Python/Dagster ETL processing 10M+ records/day and improved data freshness by 60%, with production-grade scalability patterns (async workers, containerized microservices, Kubernetes) and strong multi-tenant isolation practices.”

“Frontend engineer who led end-to-end architecture and implementation at Hostclass (startup) using Next.js/TypeScript/Twilio, including a distinctive live-session page with a 3-way screen-sharing capability. Experienced improving messy production codebases by consolidating duplicated workflow logic into reusable hooks and shipping high-risk real-time features with targeted QA and incremental rollouts under tight startup constraints.”

“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”

“Built and deployed a production AI document Q&A (RAG) platform that lets non-technical users query hundreds of PDFs/Word files, cutting search time from hours to seconds. Experienced with scaling retrieval pipelines (chunking, embeddings, vector search, batching/caching) and orchestrating reliable workflows using AWS Step Functions/Airflow with robust retries, monitoring, and fallbacks.”

“JavaScript engineer who effectively "maintains" an internal open-source-style React/Node.js shared library used by multiple teams—owning API stability, semantic versioning, CI/testing, logging, and documentation. Demonstrates strong cross-team debugging and change-management skills (schema-driven refactors, feature flags, validation layers) to ship new features without breaking existing workflows, plus a profiling/benchmarking-driven approach to performance.”

“AI Engineer at DXC Technology who has shipped production LLM/NLP systems on AWS (SageMaker, FastAPI) and optimized them for real-time latency and unpredictable traffic using quantization, batching, and autoscaling. Strong MLOps and monitoring discipline (MLflow, CloudWatch, SageMaker Model Monitor) and proven business impact—delivered models with 92% predictive accuracy and cut enterprise decision-making time by 30% through close collaboration with product managers.”

“Full-stack engineer who built an email/SMS marketing platform from the ground up using PHP (Yii2/Laravel) and JS (jQuery/Vue components), including campaign reporting, A/B testing, and extensive ESP/SMS integrations (Mailgun/Mailjet/Twilio plus AWS SES/SNS). Notably scaled the system for very large contact ingestion by migrating from MySQL to ClickHouse and implemented reliability measures like retries, logging, and rate limiting.”

“UE5 Blueprint-focused gameplay developer who shipped a VR rhythm title (Starwave), owning the player-facing star-hit interaction/feel and iterating heavily through playtests without dedicated QA. Also designed a modular tutorial/quest system by decomposing quests into reusable components so writers could assemble content independently, and has experience debugging Unreal Nav Mesh pathing issues that impacted gameplay and load times.”

“Full-stack engineer with enterprise experience at Meta System and DXC, owning end-to-end delivery of a shipment visibility portal (React UI in Liferay DXP + Java/Spring Boot REST APIs) with Dockerized deployments and automated test coverage. Has hands-on AWS work across EC2/Lambda/S3 and multiple databases (DynamoDB, RDS, Neptune, DocumentDB), plus built a Python/Flask data migration platform with validation for correctness and repeatability.”

“Full-stack engineer with healthcare/bioinformatics experience who built a real-time genomic data analysis and 2D visualization feature (React/TypeScript + D3, FastAPI) at University of Utah Health, deploying on AWS ECS Fargate with monitoring and measuring engagement via Google Analytics. Also built AWS Lambda-based ETL pipelines for lab data ingestion using pandas/NumPy with reliability patterns (idempotency, retries, CloudWatch alerting) and drove maintainability improvements through shared component libraries and React hooks.”

“Full-stack software engineer who shipped production systems in academic and e-commerce contexts, including a UC Irvine course recommendation platform with async Kafka-based OCR processing (Tesseract) and LangChain-driven recommendations. Strong in building polished React/TypeScript dashboards (Figma-to-implementation) and owning Python backends (FastAPI/Flask) with solid API design, caching, and AWS EKS deployments; delivered measurable impact (tripled engagement, ~50% faster processing).”

“Full-stack/product-leaning engineer who owned an end-to-end AI Tutor feature (Claude-powered) shipped simultaneously to iOS/Android/web via Expo, with Cloudflare Workers backend and PostHog analytics. Built the company’s GitHub-based CI/CD to coordinate app store releases with backend blue/green deployments. Also has significant data engineering experience (including ~8TB/day workloads) using dbt/Fivetran plus sharding and hashing-based diffing for correctness.”

“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”

“Backend/data engineer with strong production experience across Python microservices (FastAPI) and AWS serverless/data platforms (Lambda, API Gateway, Glue, Redshift). Demonstrates reliability and incident ownership (rate limits, retries/circuit breakers, monitoring) and has delivered measurable SQL performance gains (12–15s to <800ms, ~60% CPU reduction). Seeking fully remote work and not open to relocation/onsite meetings.”

“Backend engineer with deep experience modernizing a provider credentialing/compliance platform with multiple upstream/downstream integrations. Focused on building predictable, scalable REST APIs (primarily ASP.NET Core; framework-agnostic approach applicable to FastAPI), improving performance via DB/query optimization, and hardening workflows with idempotency, transactions, feature flags, and strong auth/RBAC patterns.”

“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.”

“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 shipped a production GPT-4 + RAG customer support chatbot that materially improved support operations (response time 4 hours to <3 minutes; ~65% tier-1 ticket automation). Demonstrates strong end-to-end LLM engineering across retrieval (Sentence Transformers/Pinecone), safety (multi-layer moderation), cost/latency optimization (caching/streaming, Celery/Redis), and rigorous evaluation/monitoring (shadow deploys, Datadog, 500+ test cases), plus proven stakeholder buy-in leading to 80% adoption.”

“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.”

“Built and productionized a clinical NLP + patient risk stratification platform at Dermanture, combining Spark/PySpark pipelines with BERT/BioBERT for entity extraction and text classification and downstream risk models in TensorFlow/scikit-learn. Experienced running regulated, auditable ML workflows with Airflow and AWS SageMaker, emphasizing data validation (Great Expectations), drift monitoring, and explainability (SHAP) to drive clinician trust and adoption.”

“Software engineer turned solutions/technical support engineer with 5+ years of experience supporting and migrating a custom CRM used by U.S. House of Representatives offices. Has hands-on ownership of database export/import scripting, API key-based integrations, and production troubleshooting, and also consults government customers on procurement/CLM workflows while partnering with sales/marketing on demos and adoption use cases.”