Vetted AWS Professionals

“Amplitude contractor focused on AI/ML product development and backend systems, with hands-on experience shipping and improving LangChain-based event classification workflows in production. They combine LLM pipeline design, AWS data infrastructure, and pragmatic human-in-the-loop controls to make analytics systems faster, more reliable, and scalable.”

“Operator-founder currently building two apps to MVP, handling everything from market research and test-user feedback to hands-on product changes in FlutterFlow and Visily. In a prior large-scale portfolio environment, they brought order to a highly disorganized 100+ person, 26-value-stream delivery organization by standardizing Jira workflows, creating stakeholder cadences, and lifting predictability from 30% to 86%.”

“Frontend engineer with JP Morgan Chase experience building data-heavy React/TypeScript products, including an AI-powered enterprise search application and workforce analytics dashboards. Stands out for combining reusable component architecture, Redux-driven state flow, responsive CSS, and production performance tuning for large-scale internal enterprise tools.”

“Data/automation-focused engineer with hands-on experience building production workflows across marketing, sales, and RevOps at ZoomInfo. They’ve owned end-to-end automations spanning Snowflake/Databricks pipelines, ad platform API integrations, LLM-powered sales prep and deal summarization, and ML-based account prioritization.”

Aakash Khepar

Screened

Mid-level Full-Stack AI Engineer specializing in agentic AI systems

Tempe, AZ4y exp

“AI/full-stack builder with hands-on experience shipping healthcare, career-tech, nonprofit, and fintech products, spanning speech AI, browser extensions, agentic RAG systems, and enterprise ML monitoring. Stands out for combining strong technical depth with measurable outcomes, including reducing clinical call WER from 26% to 3%, building safe tool-using agents with rollback/RBAC, and delivering zero-to-one multi-tenant platform features in ambiguous environments.”

“Backend/full-stack engineer with experience at Walmart Global Tech who built and deployed an end-to-end consumer quiz product independently, covering frontend flow, Spring Boot APIs, PostgreSQL, AWS infrastructure, and CI/CD. Not a native iOS or React mobile specialist, but stands out for taking products from idea to production and emphasizing maintainability, reliability, and extensibility.”

Suman Madipeddi

Screened

Junior AI/ML Engineer specializing in agentic AI, RAG, and voice systems

San Jose, CA2y exp

“Full-stack AI product engineer who has owned production-grade document intelligence and agent systems at meaningful scale, including a copilot used by 10,000+ users and 1M+ queries. Particularly strong in combining React/TypeScript product work with Python/FastAPI, RAG, knowledge graphs, observability, and performance tuning—cutting latency from ~7 seconds to 0.5 milliseconds while improving trust through citations and human review.”

“Engineering executive and former CTO with a rare blend of enterprise healthcare AI leadership and consumer AI product building for neurodiverse users. Led Adoreal’s U.S. expansion, scaled a multidisciplinary org by about 60%, modernized platform architecture with Kubernetes and CI/CD, and consistently ties engineering and AI decisions to trust, onboarding efficiency, and revenue outcomes.”

Kevin Lam

Screened

Senior Frontend Engineer specializing in React, TypeScript, and accessibility

Remote8y exp

“Frontend product engineer who has owned complex B2B SaaS surfaces end-to-end, especially operational dashboards and workflow-builder experiences at Rewst. Stands out for combining product thinking with scalable React/TypeScript architecture, API-shaping with backend teams, and a strong focus on maintainability, performance, and design-system quality.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“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 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.”

“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.”

“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 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 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.”

“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.”

“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.”