Vetted AWS Professionals

Ashwitha Reddy

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices and AWS

Ohio, United States3y exp

“Backend/platform engineer who has owned a real-time business analytics dashboard backend (Python/Flask/MongoDB) and built Kafka event-streaming pipelines with idempotent processing and DLQs. Strong DevOps/GitOps experience deploying containerized microservices to AWS EKS with CI/CD (Jenkins/GitHub Actions/CodePipeline) and ArgoCD auto-sync/drift detection, plus hands-on support for phased hybrid cloud/on-prem migrations using feature flags and replication.”

Mukul Sai Pendem

Screened

Mid-level Full-Stack Engineer specializing in cloud-native microservices and DevOps

United States (Remote)4y exp

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

“Data engineer with experience at Capital One and HSBC building and operating GCP-based data platforms. Led an end-to-end Oracle-to-BigQuery migration processing ~200–300GB/day using Dataflow/Beam, Airflow, Dataproc/PySpark, and Looker, achieving ~99.5% pipeline success and ~30% fewer data quality issues. Strong in production reliability, schema drift handling for external APIs, and BigQuery performance/serving patterns (materialized views, authorized views, versioned datasets).”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

Chakravarthy V P

Screened

Executive AI Consultant/CTO specializing in Agentic AI, GenAI, and cloud-native data platforms

Texas, USA21y exp

“Bootstrapped founder and CTO of C4Scale, a 2.5-year-old services-led company delivering MVP-to-scale product/platform builds for high-value clients across 5+ countries (10+ projects). Strong fit for roles blending scalable SaaS platform engineering, technical org leadership, and practical AI adoption, with clear awareness of the operational and GTM challenges of scaling into enterprise.”

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”

Sharath Kasula

Screened

Senior Full-Stack Engineer specializing in React, TypeScript, and real-time web applications

New York, NY7y exp

“Frontend-leaning full-stack engineer at T-Mobile who owned a real-time operational dashboard end-to-end, from Figma collaboration through React/TypeScript implementation to backend/API and SQL performance coordination. Stands out for diagnosing cross-layer production issues, improving onboarding with measurable drop-off reduction, and turning repeated product needs into reusable primitives adopted across multiple teams.”

Waldorff Villacres

Screened

Healthcare technology executive and architect with 20+ years leading enterprise platforms and digital transformation.

Seattle, Washington, USA21y exp

“Healthcare-focused founder in the R&D stage building an EHR and clinical staffing startup centered on value-based care. They have already tested the concept with the market, are engaging Medicaid/Medicare leaders and industry conferences like ViVE and HIMSS, and are focused on early-signal detection to improve patient outcomes while lowering utilization costs.”

Tong Wu

Screened

Senior Software Engineer specializing in full-stack SaaS and cloud systems

Ontario, Canada8y exp

“Built and owned Laserfiche's Schedule Custom Report platform end-to-end, spanning Angular frontend, backend orchestration, and production observability for an enterprise reporting workflow. Stands out for integrating multiple independent microservices with RabbitMQ-based event orchestration while also improving UX on complex migration tooling.”

“Full-stack product engineer with hands-on ownership of both a real-time community Q&A platform and a production payroll reorder batching system. Stands out for combining backend architecture, React frontend work, and pragmatic performance improvements, including a 2-3x speed gain through batching and thoughtful UI/UX refinements that reduced user errors.”

Kavya Aitagoni

Screened

Junior Full-Stack Engineer specializing in web platforms and live events

San Francisco, CA2y exp

“Full-stack product engineer with experience shipping user-facing web products end-to-end, including an event analytics dashboard and checkout improvements at Eventbrite. Stands out for combining frontend polish, backend reliability, and production-minded practices like idempotent APIs, query optimization, CI/CD, logging, and monitoring to improve conversion and reduce engineering dependency.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

Ronald Brockmann

Screened

Executive technology leader specializing in cloud, AI, video, and embedded systems

San Francisco Bay Area, CA32y exp

“Serial startup founder with multiple prior ventures that raised capital, now building an AI supervision product focused on child safety in online gaming. Has repeatedly operated in new technology categories such as WiFi, video over IP, cloud gaming, and AI supervision, and approaches validation through customer interviews, industry research, and bootstrapped market proof.”

“Built production AI systems in high-stakes domains, including a medical RAG chatbot focused on reducing hallucinations and a document-processing workflow that automated manual PDF extraction. Demonstrates strong end-to-end ownership across backend services, APIs, LLM integration, and iterative reliability improvements based on real usage and failure analysis.”

“Built and deployed an AI-driven expense categorization workflow integrating OpenAI API and PGVector to automate general ledger coding. Stands out for combining LLM/embedding architecture with finance operations context, stakeholder-facing deployment ownership, and measurable impact of roughly 30%+ reduction in manual coding effort.”

Rohit Vibhu Channananjundarya

Screened

Mid-level Software Engineer specializing in distributed systems and full-stack platforms

Chicago, IL6y exp

“Engineer who treats AI as a force multiplier rather than a replacement for judgment, with hands-on experience using tools like Claude Code, Cursor, Copilot, and Codex across planning, coding, testing, and review. Particularly notable for building a multi-agent PR review system that automated summarization, risk scanning, schema validation, and test suggestions, helping the team shift reviewer time toward architecture and business logic.”

“Built and shipped a production Python service integrating with a helium mass spectrometer for real-time sensing and remote control/monitoring. Combines hardware integration, observability, and Playwright-based automation expertise, with a strong track record of turning brittle or ambiguous real-world processes into reliable systems— including reducing dashboard automation failures from about 20% to under 2%.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”

“Candidate explicitly stated they do not have production agentic/LLM or generative AI experience, aside from spending a few hours becoming familiar with the process. Compensation expectation stated as 225,000.”

“AI and platform engineer with 5 years of experience who built a production knowledge assistant for Verizon end-to-end, from architecture through deployment, monitoring, and incident hardening. Stands out for combining modern LLM/RAG systems with enterprise-grade rigor, including validation layers, observability, versioning safeguards, and measurable impact on technician productivity and retrieval quality.”

“Currently building a DBC (Digital Birth Certificate) agentic AI system to speed root cause investigation for quality issues at their company. They bring hands-on experience designing and leading multi-agent workflows, including orchestrator/root-agent patterns, evaluation agents, clarification agents, and practical guardrails for hallucination, bias, and rate-limit management.”