Vetted AWS IAM Professionals

“Backend/cloud engineer with hands-on ownership of a climate data extraction pipeline (BeautifulSoup + Pandas ETL + CRON) that automated 50k+ monthly data points and removed ~20 hours/week of manual work. Also built a multi-AZ Kubernetes deployment for a Node.js system using Terraform and GitHub Actions (blue-green, rollbacks) and has Kafka/FastAPI experience from a healthcare plan management project.”

“Founder/CTO-type candidate with deep exposure to the VC/accelerator ecosystem (Open Angel Forum; HAX/SOSV) and a track record of raising capital, including a $1M seed round in 2017. Built ventures spanning IoT wearable/HUD SaaS for industrial hygiene monitoring and institutional Bitcoin mining/distributed compute infrastructure, and ran a venture from 2017–2025.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

Isaiah Johnson

Screened

Senior DevSecOps/Cloud Engineer specializing in secure AWS delivery for federal environments

Alexandria, VA8y exp

“Cloud-focused DevSecOps/infra engineer with strong AWS production ownership (EC2/EKS/ECS) and hands-on CI/CD (Jenkins->ECR->Helm on Kubernetes). Demonstrated end-to-end outage recovery (ALB 503s caused by Helm env var misconfig) with rapid rollback plus pipeline guardrails, and deep Terraform experience (modular IaC, remote state with S3/DynamoDB, drift detection) supporting federal cloud modernization efforts.”

Anudeep Reddy Veerla

Screened

Mid-Level Software Engineer specializing in cloud-native microservices

Boston, MA3y exp

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

Purva Chakravarti

Screened

Senior Full-Stack Software Engineer specializing in scalable web apps, cloud, and blockchain/AI

Chino, California11y exp

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“Backend/full-stack engineer with experience spanning AI-powered fintech features and high-volume roadway surveillance/ALPR platforms. They have owned production APIs, improved API latency by 35% through database/query optimization, and helped drive a Kafka-based ingestion architecture that increased throughput by 30% while improving reliability at scale.”

“Software engineer with end-to-end ownership of a Qt/C++/QML desktop app for monitoring/configuring equipment, including hands-on UI performance optimization. Also built a web-based AI agent interface (React/TypeScript + Python Flask) with strong API contract discipline and async state handling, and improved microservices reliability using idempotency, DLQs, and observability. Created an internal CI/CD automation tool adopted across engineering and operations teams, adding safer rollbacks and better error messaging based on feedback.”

“Full-stack intern at Rebel who owned backend work on a cross-platform music platform using Python/Django with MongoDB, implementing user-focused REST APIs end-to-end. Also built CI/CD pipelines (Jenkins/GitHub Actions) to containerize and deploy to AWS, and has experience integrating Kafka-based real-time event processing with reliability and observability practices.”

“CloudData engineer who productionized an LLM assistant for a warehouse/logistics customer by wrapping it as a versioned, containerized API with guardrails, deterministic post-processing, and full observability. Experienced diagnosing real-time RAG/agentic incidents (latency spikes and confident-wrong answers) using trace-based isolation, replay in staging, retrieval tuning, and canary releases. Regularly runs technical demos/workshops and partners with sales on security/IAM, SLAs, and pilot rollouts to drive adoption.”

“Cloud-focused application security practitioner with hands-on AWS and Kubernetes experience, including securing a manufacturing monitoring platform (API auth, least-privilege IAM, CI/CD security checks) and troubleshooting a production data-ingestion outage caused by an overly restrictive IAM change. Experienced in implementing cloud-native security tooling (IAM Access Analyzer, Inspector, CloudWatch) and deploying monitoring/security agents via Kubernetes sidecars with Helm, Prometheus/Grafana, and Jenkins-driven CI/CD.”

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

“Cloud/infrastructure engineer with strong production operations background across AWS, Azure, and Kubernetes, supporting 30+ enterprise workloads for ~40,000 users. Demonstrated incident leadership (hybrid AKS-to-AWS routing outage) with a reported 60% MTTR reduction, plus hands-on CI/CD (Jenkins) and Terraform-based IaC for AWS (VPC/EC2/EKS). Lacks direct IBM Power/AIX/PowerHA experience but emphasizes transferable ops and troubleshooting skills.”

“Data engineer who has owned production pipelines end-to-end—from Kafka/Airflow ingestion through SQL/Python validation and dbt transformations into Redshift/BI. Also built and operated a large-scale distributed web scraping platform (50–100 sites daily, ~5–10M records/day) with Kubernetes, Kafka queues, robust retries/DLQ, anti-bot measures, and backfill-safe raw HTML storage.”

Hammad Ur Rahman

Screened

Senior DevOps Engineer specializing in cloud infrastructure and CI/CD

Boston, Massachusetts11y exp

“IBM Power/AIX engineer who has owned a 150+ LPAR AIX 7.x estate with VIOS/HMC/vHMC and PowerHA in production, including real outage and failover recoveries. Also brings modern DevOps/IaC experience—built Jenkins pipelines deploying to AKS and implemented Terraform on AWS with remote state, locking, and drift management.”

Ashay Panchal

Screened

Mid-level Software Engineer specializing in AI-driven distributed systems

San Jose, CA4y exp

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

Mrudula Devaguptapu

Screened

Mid-Level Software/AI Engineer specializing in backend systems, data pipelines, and RAG automation

United States3y exp

“Backend engineer with experience modernizing high-traffic subscription and payment systems (TCS) by moving to event-driven Spring Boot microservices with Kafka, adding idempotency/state management to eliminate duplicate processing. Built and scaled FastAPI services for AI automation workflows (360DMMC) with versioned contracts, JWT security, and strong observability, and has led live refactors using feature flags, parallel runs, and data reconciliation.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“Engineer with hands-on experience modernizing healthcare platform authorization and EVV compliance workflows, including replacing hardcoded permissions with a Cerbos-based RBAC/ABAC system. Stands out for pragmatic AI-assisted development in regulated environments, with a strong emphasis on testing, auditability, and catching subtle business-rule failures before production.”

“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.”

Atharva Jagtap

Screened

Junior Full-Stack Software Engineer specializing in cloud and AI/ML applications

United States, Remote2y exp

“Full-stack engineer with hands-on experience across e-commerce personalization, enterprise RAG assistants, and cloud infrastructure automation. They’ve shipped AI features using Azure LLM APIs and vector search, improved recommendation engagement, and worked across frontend, backend, ML-informed analytics, and AWS infrastructure in early-stage environments.”

“Full-stack engineer with startup SaaS experience building workflow automation and case management platforms for business operations teams. Strongest in Python, TypeScript/React, and PostgreSQL, with hands-on ownership from backend architecture and APIs to production deployment on AWS; notably helped reduce manual processing and improve customer turnaround times in a high-ambiguity scaling environment.”