Vetted AWS Professionals

“Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.”

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

“Full-stack engineer with experience building customer-facing fintech mobile features end-to-end (loan estimate comparison) and scaling event-driven microservices in enterprise environments (Verizon). Has designed TypeScript/React/Node systems with queues/caching and built an internal rule-engine for bulk Excel ingestion that reduced data errors and manual rework through automated validation.”

“CoreAI intern at The Home Depot who improved the Magic Apron Assistant by building a production video ingestion + RAG retrieval system for long videos (uploads and YouTube), including a graph-based retrieval module to speed up and improve relevance. Experienced with Kubernetes orchestration (HPA) and production reliability practices like caching, monitoring, regression testing, and stakeholder-driven requirements.”

“Frontend/product engineer who has led end-to-end builds across automotive and healthcare: created a multi-tenant, high-performance Next.js luxury inventory platform and a secure, Stripe-powered sick-note workflow integrated with an EMR. Known for data-driven UX decisions (A/B testing) and pragmatic modernization of critical systems (3DS2 upgrade) with measurable conversion and risk improvements.”

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

“Frontend engineer who led Later’s end-to-end “Social listening” product build, driving architecture decisions through close collaboration with product, design, and backend. Also spearheaded a staged JS-to-TS migration by running knowledge-transfer and pair-programming sessions, and ships quickly with CI/CD, feature flags, and strong automated testing practices.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Backend-focused engineer who owned an end-to-end Python/Flask service at Viasat powering a 1000+ user internal React app, including API design, Postgres performance tuning (~50% faster), Dockerization, and CI/CD. Demonstrated strong problem-solving by building custom EDN parsing logic and has built near real-time AWS SQS/Lambda pipelines with DLQs and autoscaling patterns; currently ramping on Kubernetes/GitOps (ArgoCD).”

“Backend engineer who owned an order management API built with Python/FastAPI and PostgreSQL, integrating payment and shipping providers with strong reliability patterns (idempotency, async workers, retries/backoff, circuit breakers). Experienced deploying services to Kubernetes using a GitOps model with ArgoCD (auto-sync, self-healing, pruning, rollbacks) and building high-volume Kafka streaming pipelines. Has also supported phased cloud-to-on-prem migrations with a focus on security monitoring/SIEM log continuity.”

“Built an AI-powered loan automation dashboard using React and open-source JavaScript libraries, with hands-on experience improving real-world performance by reducing re-renders and optimizing/caching multiple API calls. Also produced developer-friendly API documentation for a voice assistant project, helping teammates integrate features faster with fewer errors.”

“Built ReflectlyAI, an AI-powered interview coach, implementing a low-latency Python/Flask backend with modular LLM/Whisper services, retries/fallbacks, caching/batching, and async/background processing. Demonstrates strong PostgreSQL/SQLAlchemy performance tuning (EXPLAIN ANALYZE, composite indexes, selectinload) and multi-tenant isolation patterns (tenant-scoped schemas, tenant_id middleware), reporting ~50% response-time reduction.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Software engineer with hands-on experience building a decentralized file-sharing dApp, bridging a React frontend with Ethereum smart contracts via Web3.js and integrating IPFS for decentralized storage. Demonstrates a rigorous, measurement-driven approach to performance optimization (profiling + benchmark/regression loop) and strong ownership in high-stakes environments, including Fircosoft sanctions platform optimization and rapid production hotfixes for user-impacting issues.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“Software engineer with experience at Wipro Technologies and Wells Fargo building React-based SPAs, reusable component libraries, and developer documentation. Demonstrated strong performance engineering (React.memo, list virtualization, code splitting) with reported >50% rendering-time improvement, plus hands-on production support by diagnosing API outages via monitoring/logs and implementing traffic/server fixes. Comfortable leading workstreams in fast-changing environments using Kanban and tight stakeholder feedback loops.”

“Founder of TimeTick (timetick.io), an AI-powered diagnostics platform for IoT combining device simulation, automated testing, and real-time monitoring—initially focused on EV charger diagnostics. Former VP of Engineering with a track record of building IoT systems from scratch and applying AI to detect protocol-failure patterns that drive downtime; currently supporting existing customers and converting pilots (with leads like Siemens and ABB) into paid subscriptions.”

“Full-stack developer with strong cloud/on-prem focus (AWS, VPC networking) who has improved production reliability by bringing manually created IAM/security group resources under Terraform and standardizing environments. Demonstrated end-to-end troubleshooting across app + infrastructure + networking (traffic capture revealed proxy response truncation) and delivered Python-based monitoring/reporting enhancements that improved ops visibility and turnaround.”

“iOS full-stack/mobile engineer who built a SwiftUI (MVVM) barcode-scanner app using VisionKit for on-device, low-latency recognition, focusing on responsiveness via continuous scanning and debouncing. Also has Capgemini customer-facing support experience resolving restored-file access/permissions issues and receiving positive CSAT feedback.”

“Built and deployed a production multimodal language translation platform (text-to-text, speech-to-text, text-to-speech) using fine-tuned pretrained models (NLLB, XLSR), MLflow-orchestrated pipelines, and Docker/Kubernetes on AWS. Worked closely with non-technical linguists to tackle data cleaning and dialect variation in minority languages, improving accuracy through consistent evaluation and monitoring.”

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”