Vetted Amazon CloudWatch Professionals

“AI-focused software engineer who has built production CRM intelligence features including audio transcription, summarization, and action-item extraction, plus a multi-agent LLM/NLU pipeline using Supabase, Node.js, RabbitMQ, and CloudWatch. Stands out for a disciplined approach to AI-assisted coding: treating AI like a junior developer, rigorously testing outputs, and refining prompts to prevent hallucinations in real business workflows like resume screening.”

“Built GRETA, a full-stack multi-agent AI platform for SEO content analysis and blog-writing support, combining React/TypeScript, serverless GCP Cloud Run workflows, and LLM/tool orchestration at scale. The system reportedly reduced manual analysis by 60%, and the candidate shows strong hands-on experience shipping AI products in ambiguous environments and refining them through internal user feedback.”

“Full-stack engineer with strong DevOps/AWS experience who ships end-to-end React/TypeScript + Node/Python systems and operates them in production. Built an LLM-assisted recommendations workflow for a SaaS product with robust reliability controls (schema-validated JSON outputs, fallbacks, caching, monitoring) and measured impact via adoption, time saved, and override rates; also experienced delivering MVPs fast in early-stage startup ambiguity.”

“Backend engineer experienced in evolving high-scale legacy on-prem systems into cloud-native, event-driven microservices on AWS/Kubernetes (noted peak traffic ~1.5M QPS). Strong focus on reliability engineering and operational excellence—SLO-driven observability, GitOps/canary rollouts, chaos testing, and preventing cascading failures (e.g., retry-storm mitigation).”

“Backend/data engineer with hands-on production experience across Azure and AWS: built FastAPI + PostgreSQL services with Azure AD OAuth2/JWT auth and strong reliability patterns (timeouts, retries, correlation IDs). Delivered AWS Lambda/ECS solutions with Terraform/CI-CD and cost controls (SQS buffering, reserved concurrency), and built/operated AWS Glue ETL pipelines into Redshift while modernizing legacy SAS reporting into Python microservices with parity testing.”

“Frontend-focused engineer with internship experience at eXp Realty and Pulse InfoFrame, building survey platforms and a drag-and-drop survey management UI in React + TypeScript. Notably helped execute a post-deployment migration from DynamoDB to Amazon RDS to enable scalable SQL aggregation (tested with ~70k survey instances), and monitored rollout via CloudWatch/Cloudflare logs while driving QA and load testing.”

“Built and deployed an LLM-to-SQL automation system in a closed/internal environment, using a retriever–reranker–validator architecture on Kubernetes with strong security controls (semantic + rule-based validation and RBAC), achieving 99% uptime and cutting manual query time ~40%. Also worked on genomic sequence classification and semantic search workflows, orchestrating data prep with Airflow, tracking/deploying with MLflow, and optimizing distributed multi-GPU training on a university Kubernetes cluster.”

“Backend/infra-focused engineer who owned production systems for distributed ML experimentation (hyperparameter tuning across a cluster with GPU scaling, custom scheduling, and checkpoint-based fault tolerance). Also built and operated a low-latency log validation service using queued async workflows with idempotency, retries/backoff, and strong observability, plus experience building resilient Selenium-based browser automations for complex multi-step web flows.”

“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.”

Lakshmi Meghana

Screened

Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP

Bristol, PA4y exp

“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”

Tao Chen

Screened

Senior Full-Stack Engineer specializing in AI-powered web products

Mountain View, CA7y exp

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”

“Built and shipped a personal LLM-powered news aggregation platform (Clear Brief) that scrapes ~200 articles per cycle, clusters them into ~15–30 consolidated stories, and supports on-demand deep dives via a Next.js API route. Emphasizes production-minded reliability (token/cost controls, timeouts, graceful frontend degradation) and database-backed orchestration using SQLite with retry + exponential backoff for burst processing.”

“Analytics professional with hands-on experience turning messy CRM and campaign data into standardized reporting datasets using SQL and Python. At EcoSolutions/EcoServices, they built reusable KPI pipelines, dashboards, and metric definitions that reduced manual reporting and improved time to conversion by roughly 55% through better segmentation and campaign optimization.”

“Built and owned enterprise data/integration deployments and production AI workflows, including a Python-based migration pipeline that moved 2M records with major improvements in onboarding speed, error rate, latency, and uptime. Also shipped a financial RAG assistant over 50K documents with sub-second p95 latency, showing a strong blend of customer-facing deployment ownership, data engineering, and LLM systems expertise.”

vinod yendluri

Screened

Mid-level MLOps Engineer specializing in production machine learning systems

Hyderabad, India3y exp

“Built an end-to-end churn prediction platform at Freddi's Flowers spanning Spark ETL on AWS, model serving, monitoring, and a stakeholder-facing dashboard. Stands out for combining MLOps rigor with product thinking—adding explainability, action-oriented workflows, and config-driven multi-tenant architecture while improving latency and automating drift response.”

“Full-stack engineer with experience spanning enterprise financial services and biomedical AI systems. They’ve shipped React/TypeScript and Spring Boot products with measurable production impact, contributed to an AWS-deployed LLM/MCP service for governed research data on Synapse, and also built a zero-to-one AI-powered Terms of Service and privacy policy crawler from scratch.”

“Built and launched his own free-to-play mobile trivia games (Unity/Xcode/Android Studio), monetizing through IAPs and multiple ad networks (AppLovin/Chartboost/AdMob). Combines hands-on implementation with growth and analytics rigor—Facebook Ads + ASO, and daily tracking of retention, crashes, conversion, and LTV:CAC—plus a clear approach to A/B testing and live events tooling.”