Vetted AWS Professionals

“Automotive software and OTA/infotainment platform leader who has repeatedly built new lines of business as an intrapreneur—most recently taking an infotainment app marketplace from concept to production in <7 months with $3M seed funding and delivering ~$200M ROI while scaling the team from 0 to 90. Deep hands-on experience solving OTA fragmentation across ECUs/telematics and multiple OS/backends, with 18 patent processes submitted; exploring an AI-driven platform to automate OTA software qualification and cut release cycles from 9–18 months to ~2 weeks.”

“Backend/platform engineer with experience across C3.ai (supply chain demand planning) and Amdocs (telecom), working on large-scale data systems and microservices. Has driven first-time adoption experiments of Snowflake + Spark to handle billion-record workloads, built Jenkins-to-Kubernetes delivery pipelines with Nexus artifact management, and implemented Kafka streaming between microservices with HA and retry/error-handling patterns.”

“Lead software engineer (3+ years) who built and scaled an AI product backend at Cosmo AGI from the ground up using FastAPI/Postgres/Redis/vector DB, targeting sub-200ms latency and supporting 1000+ active users. Strong in production-grade security and observability (OAuth/JWT, RBAC, Postgres RLS, Prometheus/Sentry), plus DevOps automation (Docker, GitHub Actions, blue-green deploys) with measurable impact on uptime, incidents, engagement, and deployment speed.”

“Backend/data engineer who has built production Python microservices (FastAPI) and AWS-native platforms for event ingestion and analytics, combining ECS/Fargate + Lambda with CloudFormation-driven environments and strong secrets/IAM practices. Experienced modernizing legacy logic with parallel-run parity validation and safe phased cutovers, and has demonstrated measurable SQL tuning wins (20–30s down to 1–2s) plus incident ownership in Glue/Step Functions ETL pipelines.”

“Software engineering intern at China Telecom who built and continuously evolved a real-time transaction platform ("Smart Tangerine") focused on strong consistency and peak-hour concurrency. Implemented microservices with Redis and RabbitMQ to decouple heavy processing and cut latency (~80ms to ~30ms), and led a zero-downtime migration from a monolith using strangler pattern, dual-write, and traffic shadowing.”

“Python/AWS engineer focused on batch-processing and data workflows, including building reusable S3/boto3 utilities with reliability features and IAM-based auth. Has led low-risk legacy modernizations using parity testing plus a month of parallel production runs, and has owned production issues end-to-end (including fixing a client-side Excel macro) while contributing to significant AWS cost reductions (~$10k/month).”

“Backend engineer with Citigroup experience who built and evolved a self-service user provisioning/identity backend, cutting onboarding from 45 minutes to under 2 minutes. Demonstrates strong production-grade integration and reliability practices (isolated integrations, retries, rollback logic, heavy logging) plus secure API development in Python/FastAPI with OAuth scope-based authorization and incremental, low-risk rollout strategies.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”

“Built and owned Prism, a real-time collaborative coding platform, making key architectural choices around deterministic event ordering and a backend source-of-truth to improve trust under concurrent edits. Also created a Python-based bug analysis and test automation suite that became part of standard engineering workflow, cutting debugging time by ~95% while improving fault detection coverage.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Built and deployed a production GenAI internal support agent at Bank of America (“Ask GPS/AskGPT”) using RAG on Azure, focused on reducing escalations and improving response quality for repetitive knowledge-based queries. Demonstrates strong production LLM engineering: custom LangChain orchestration, retrieval tuning to reduce hallucinations, rigorous offline/online evaluation, and model benchmarking with dynamic routing (e.g., GPT-4 vs Claude).”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“Former founder who built a revenue-generating DevOps GTM service from zero, using milestone-based revenue targets and multi-channel selling (relationships, channel partners, and major conferences like AWS events and Dreamforce). Also led a cross-functional FedRAMP Moderate readiness strategy to enable selling into regulated environments, coordinating engineering/product/finance/sales/security/support and third-party partners under a tight timeline.”

“At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“At Fileread, the candidate built and deployed an LLM-powered legal document classification and retrieval layer for an agentic extraction system that turns unstructured legal PDFs into structured tables with line-level citations. They productionized a RAG-style pipeline (ingestion, embeddings, retrieval, reranking, generation) and report 95%+ F1 across 70+ legal categories, emphasizing rigorous evaluation and close collaboration with legal domain experts for high-stakes precision.”

“Software engineer with deep experience in real-time payments and event-driven microservices. Built a React/TypeScript + Spring Boot system using RabbitMQ, and created an internal operations dashboard that improved visibility into message-processing workflows for engineering, support, and SRE. Strong in experimentation-driven product iteration (feature flags/A-B tests) and in scaling reliability via idempotent consumers and end-to-end observability.”

“Amazon engineer who built a production AI agent platform (Python/AWS Strands on Bedrock) that lets teams create tool-using, multi-agent workflows—e.g., agents that auto-triage and resolve customer support tickets by reading internal documentation and collaborating with a research agent. Previously worked in Deloitte on IAM using Ping Identity/Ping DaVinci orchestration, and applies orchestration thinking plus structured evaluation (LLM-as-judge, surveys, automated tests) to improve agent reliability.”

“AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.”

“Full-stack developer (Energywell) who led an internal admin dashboard end-to-end using React/Redux and a Go microservice, emphasizing performance (reduced calls, preload data) and maintainable architecture (modularity, refactoring, PR reviews). Also shipped a Redis-based caching whitelist feature in a fast-paced environment and helped implement a responsive, brand-configurable onboarding/signup frontend.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“Early-career (fresher) candidate who built and deployed a production AI medical document chatbot using a RAG architecture (LangChain + Hugging Face LLM + Pinecone) with a Flask backend on AWS EC2 via Docker. Has experience troubleshooting real deployment constraints (model dependencies, disk space, container stability) and setting up continuous-style evaluation with fixed query test sets tracking relevance, latency, and error rate.”