Vetted AWS Lambda Professionals

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

“AI Automation Engineer at BalancedTrust who single-handedly shipped production LLM features for FinTech compliance: a policy gap-analysis pipeline (SOC 2/GDPR) and a RAG-based regulatory chatbot. Deeply focused on reliability in high-stakes legal/compliance settings, with strong production engineering (edge functions, parallelized batching to cut latency, structured JSON outputs, guardrails, and monitoring) and close collaboration with non-technical compliance experts.”

“AI engineer at Cleveland Clinic building production LLM/NLP systems for radiology documentation, focused on HIPAA-aware, real-time performance across ~298 campuses. Re-architected infrastructure with AWS event-driven services to handle scaling and improved SLA compliance ~40%, and complements this with a personal multi-agent debate system (CrewAI) using local Llama/Mistral plus rigorous evaluation (A/B tests, red teaming, observability).”

“Built and deployed a domain-specific LLM chatbot for research/support, cutting manual effort by ~50%. Demonstrates strong applied LLM engineering: RAG, prompt grounding with citations and fallbacks, embedding/top-k tuning, and production monitoring (confidence, latency, feedback loops). Experienced orchestrating agent workflows with LangChain-style pipelines and continuous evaluation to maintain reliability.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”

“Hands-on engineer focused on productionizing LLM-powered assistants: builds RAG pipelines with guardrails, response schemas, and citation-grounded outputs, then hardens them with explicit NFRs (latency, uptime, security, cost). Experienced diagnosing agentic/LLM workflow issues in real time using observability and stepwise isolation, and supports go-to-market via developer demos, workshops, and pre-sales technical evaluations in microservices/Spring Boot environments.”

“Backend-leaning full-stack engineer who has built and operated event-driven microservices platforms (FastAPI/React/TypeScript, Kafka, Kubernetes) and internal DevOps tooling. Delivered measurable impact through user-feedback-driven iteration (WebSockets update mechanism cutting redundant API calls ~30%) and operational improvements (deployment monitoring dashboard reducing rollback time ~40%), with strong focus on reliability, observability, and data consistency at scale.”

“Backend-focused Python engineer who builds modular Flask services on AWS and specializes in performance/scalability work across data-heavy APIs. Has concrete wins in query optimization (1.5s to <200ms) and high-throughput async processing (Celery+Redis, ~40% throughput gain), plus experience serving scikit-learn text classification models via containerized REST services and designing multi-tenant data isolation strategies.”

“Backend engineer who built an AI RAG quoting system for the fastener industry, reducing quote turnaround from weeks to ~30 minutes and raising retrieval accuracy to ~90% by solving a semantic-collision issue with a parent-document retrieval design. Strong in production AWS integrations (Cognito auth, S3 pre-signed uploads), performance optimization (multithreading/out-of-core), and real-time streaming (Kafka/Spark Kappa architecture achieving sub-second latency), plus Kubernetes logging and GitHub Actions CI/CD to ECR.”

“Backend engineer focused on cloud-native microservices on AWS, owning Python/Flask ingestion services integrated with S3/Lambda and deployed via Docker/Kubernetes with CI/CD. Has led phased migrations from manually managed EC2 setups to automated CloudFormation + pipeline-driven releases, and designed event-driven near-real-time pipelines with idempotency, retry/backoff, and strong observability.”

“Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).”

“JavaScript engineer with open-source experience on a database visualization library, focused on real-time rendering performance for large datasets (virtualized DOM rendering, requestAnimationFrame/debouncing, memoization) and on raising project quality via tests and CI performance benchmarks. Also built Kafka-based messaging documentation and sample producer/consumer apps to speed onboarding, and has experience diagnosing production issues including concurrency-related duplicate data problems.”

“Python backend engineer who owned an end-to-end Django/DRF authentication and account-management module (JWT, RBAC, email verification) and optimized token validation performance. Has hands-on Kubernetes + Helm delivery with GitOps via ArgoCD (multi-environment app-of-apps, drift detection/rollback) and has supported a cloud-to-on-prem migration using staged testing and phased cutover. Also built and scaled a Kafka-based real-time user activity tracking pipeline with reliability and backpressure controls.”

“AI/ML engineer (Merkle) with hands-on experience deploying RAG-based LLM applications and real-time recommendation engines into production. Strong in cloud/on-prem architectures, GPU autoscaling, caching, and network optimization—delivered measurable latency reductions (40–70%) and improved retrieval relevance by systematically benchmarking chunking/embedding configurations and validating pipelines via CI/CD.”

“LLM solutions practitioner with SOC/alert-triage experience who takes LLM prototypes to production using RAG (Pinecone), FastAPI services, guardrails, CI/CD, monitoring, and robust fallback logic. Known for rapid real-time debugging of embedding/vector and agent workflow issues, and for driving adoption through code-first workshops and sales-aligned custom demos with measurable improvements (35% faster triage; 40% increase in correct tool usage).”

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

“Software engineer with healthcare domain experience (patient monitoring and provider systems) who improves reliability and performance in complex React/Flask applications. Led API standardization for shared internal React utilities using an RFC + deprecation strategy, and optimized a live WebSocket dashboard to handle 3000+ concurrent clinics while reducing client CPU usage. Strong in production debugging, data ingestion validation, and operational improvements like structured logging and alerting.”

“Full-stack engineer with Deloitte and CrowdDoing experience shipping production web platforms on AWS (EC2/RDS/S3/Fargate) using React/TypeScript and Node/Express/PostgreSQL. Built customer-facing authentication/SSO flows (OAuth2 + JWT) and state-specific US privacy consent workflows, and also delivered a Python/Flask LLM-based finance document parser chatbot with vector DB integration and latency optimizations.”

“ML engineer with General Motors experience deploying production AI systems, including a BERT-based sentiment classifier for over a million customer support call transcripts (reported ~91% precision) and sub-200ms latency via FastAPI/Docker optimization. Also built predictive maintenance models and automated retraining/monitoring workflows using Airflow and MLflow, collaborating closely with non-technical customer support stakeholders.”

“Backend/infrastructure-focused engineer building AI-agent products for small businesses, including a customer-service agent platform with intent routing, RAG over Pinecone, and external booking API integration. Has shipped Python/FastAPI services with JWT auth, versioned APIs, Docker deployments to AWS EC2 via GitHub Actions, and production monitoring with Prometheus/Grafana.”

“Full-stack engineer with strong fintech/financial-systems experience who built an automated reconciliation and reporting system end-to-end (Python/Django/FastAPI + React, Docker on AWS), cutting reconciliation time by ~25% and improving audit traceability. Experienced designing typed REST APIs and Postgres data models, operating production workloads on AWS (EC2/Lambda/RDS/DynamoDB/CloudWatch), and building reliable ETL/integration pipelines with idempotency, retries, and reconciliation.”

“Technical/product lead at Albanero (acquired by Infor in 2024; now at Infor) who built a Data Mesh-focused “Governance as a Product” module from early persona-based policies through a highly configurable multi-ERP governance platform (MDM, multi-source mastering, match/merge, automated review workflows). Also troubleshoots agentic/LLM workflows in production using auditability, guardrails, monitoring, and real-time validation—fixing a P0 false-positive security flagging issue and contributing to significant deal/adoption growth (~50%) after V2 launch.”

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”