Vetted AWS Professionals

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

“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.”

“Experience spans college-built AWS-hosted Python/Flask web apps and enterprise data work at General Motors, including PostgreSQL query optimization on millions of records and multi-tenant-style data isolation using group-based, column-level permission grants. Also built an AWS-hosted meat price prediction dashboard using Dash/Plotly and ran large nightly data pipelines orchestrated with Apache Airflow.”

“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).”

“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”

“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.”

“Engineering leader with a strong automation-first philosophy ("special treatment doesn't scale"), experienced in building self-service tooling and communicating clearly with executives via BLUF-style updates. Has delivered end-to-end business-driven solutions—from sourcing alternative vendor data to installing infrastructure and writing drivers/analytics—and led pragmatic architecture changes in R/Rserve that significantly improved performance while driving cloud costs toward near-zero.”

“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.”

“Defense-domain AI engineer who built a production ReAct-style RAG system for military training data/material generation, scaling to ~1000 users and cutting generation time by 50%. Also has experience designing GPU-cluster parallel computation with PyTorch and handling production incidents involving database performance and schema design.”

“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.”

“AAA sports game QA tester who supported full development through launch and live updates, owning gameplay stability/regression risk. Experienced in triage-driven prioritization and in diagnosing complex crash issues (including thread synchronization) using evidence-backed Jira reports, then hardening coverage with stress/concurrency/soak and CI-integrated regression suites.”

“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.”

“Cloud-native infrastructure and application security practitioner focused on AWS multi-account environments, partnering with engineering teams on threat modeling, secure architecture reviews, and secure SDLC alignment. Has implemented IaC-driven centralized logging/SIEM and improved IAM and logging redundancy to strengthen both security posture and operational resilience, plus maintained secure CI/CD pipelines with integrated security scanning and release guardrails.”

“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.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

“Cloud/DevOps engineer operating across AWS and Azure, running Kubernetes workloads with secure CI/CD (GitHub Actions/Azure DevOps) and Terraform IaC. Has supported AIX/PowerHA systems in hybrid environments—handling failover testing, incident recovery, and performance troubleshooting (including multipath/storage-path issues)—and has led cutovers by managing dependencies, rollback, and stabilization.”

“QA engineer with 12 years of experience in SaaS/platform releases (not AAA games), partnering closely with developers and product through standups, 1:1s, and Three Amigos. Owned high-risk areas like payment data integrity and critical user journeys, running CI-based full regression to support go/no-go decisions. Notably caught a production EV charging receipt defect (zero charge shown) and drove it to resolution with strong Jira documentation and cross-functional escalation.”

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

“Full-stack TypeScript/Angular/Node engineer who owned a production healthcare application for a pharmaceutical client, supporting 100K+ monthly users across 10+ countries. Strong focus on maintainability and quality (reusable localized component library, ~90% unit test coverage, SonarQube in CI/CD) plus performance work (reported 15% client-side latency reduction and up to 50% backend latency reduction) while migrating legacy mobile code with strict backward compatibility.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Data engineer with banking FP&A experience who led an end-to-end migration of 10+ TB from Teradata to Azure (ADF + Data Lake + Databricks/PySpark + Synapse). Emphasizes reliability (multi-stage validation, monitoring/alerts) and performance (Spark tuning, incremental loads, autoscaling), reporting ~99.5% pipeline reliability while supporting downstream consumers with stable schemas and clear change management.”