Vetted Amazon S3 Professionals

“Founding Principal Engineer/Head of Engineering at Amberflo (Seed $5M Homebrew; Series A Norwest) who built and shipped an AI Gateway + real-time LLM cost metering/pricing MVP end-to-end (control plane/data plane, AWS infra, CI/CD). Known for extremely fast MVP cycles (often 1–2 weeks), scaling teams (50–60 hires), and driving major pivots (usage-based billing to FinOps) by repurposing an existing metering/pricing platform; based in Chicago and has led a Silicon Valley startup remotely with frequent Bay Area travel.”

“Platform-focused engineer/technical program leader working in silicon/wafer validation environments, with hands-on experience securing access to sensitive test results and engineering tooling. Has implemented RBAC/least-privilege controls with Azure Entra ID, Key Vault, PAM and integrated Checkmarx into dev workflows, while also deploying ML services on AKS using Bicep/Helm/Docker and Azure DevOps CI/CD with strong monitoring and incident response practices.”

“Backend/data engineer focused on AWS-based Python services and data pipelines: built a Django/DRF user management/auth platform deployed with serverless AWS (Lambda/API Gateway) and event-driven workflows (Step Functions/EventBridge), with CloudFormation + Jenkins for automated delivery and Secrets Manager/Parameter Store for secure config. Also delivered AWS Glue ETL from S3 to RDS with schema evolution controls and incident-driven improvements, and has demonstrated measurable SQL tuning impact (minutes-to-seconds).”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Frontend-focused engineer operating at a staff level with experience at Amazon and startups, known for rescuing high-impact, frontend-heavy systems through architecture, performance, and quality improvements. Delivered outsized results including cutting load times from ~90s to ~3s, raising test coverage from <1% to >80%, and enabling multi-team adoption of modern state management via training sessions for 50+ engineers.”

“Software engineer with Capital One experience contributing to shared internal “open-source style” JavaScript/React/TypeScript libraries (component library and hooks/utilities). Drove measurable performance gains (~25% improvement) by refactoring hooks to prevent unnecessary re-renders, and improved adoption via stronger documentation, testing (Jest), semver discipline, and code review/PR workflows; also stabilized a backend service by adding monitoring and automated tests in an unstructured project.”

“Built a production internal RAG-based Q&A assistant at Huawei for ~4,000 engineers over a 12M-document Elasticsearch corpus, replacing link-only search with synthesized answers and achieving 87% user acceptance while keeping hallucinations under 0.4%. Pairs rigorous offline benchmarking (RAGAS, PR-gated F1 improvements) with human A/B testing and OpenTelemetry-based production monitoring, and also has strong Kubernetes/SRE experience orchestrating 50+ gRPC services with major MTTR and pager-fatigue reductions.”

“Backend engineer focused on building production-ready Python services (Flask/FastAPI) with strong performance and scalability instincts—Celery/Redis background processing, robust multi-tenant isolation (Postgres RLS), and pragmatic CI/Docker operations. Demonstrated measurable DB optimization impact (cut a critical analytics query from ~1–2s to ~100–150ms) and has hands-on experience integrating LLM/ML workflows (OpenAI, LangChain, embeddings, Redis/FAISS vector stores) without degrading API responsiveness.”

“Backend/DevOps-focused engineer with healthcare and financial systems experience, including an ICU readmission risk platform delivering real-time ML scores via a secure FastAPI service (PyTorch model serving, PostgreSQL, Celery/Redis) deployed on AWS with strong observability. Has hands-on Kubernetes GitOps delivery (Helm, ArgoCD, HPA) and has supported a JPMC on-prem-to-AWS microservices migration using phased validation and blue-green cutovers, plus Kafka/Avro streaming for real-time transaction processing.”

“Backend/automation engineer who led the design of an OOP Python test automation framework for AWS infrastructure (Behave + Jenkins), cutting regression effort from weeks to a 3–4 hour run. Has hands-on cloud and DevOps experience across AWS (boto3, ECS, AMI automation via GitHub Actions) plus data/migration work including on-prem-to-cloud Oracle Retail DB migration with rollback planning and a Kafka + ML fraud-detection streaming pipeline.”

“Full-stack engineer with strong DevOps/AWS production experience who builds and operates multi-agent AI systems end-to-end (Streamlit/Python through Docker/Kubernetes and ECS/Fargate). Has delivered measurable outcomes: sub-2s latency and ~92% routing accuracy for an AI wellness assistant, shipped an AI-for-BI prototype in under 6 weeks cutting analysis time ~40%, and improved pipeline iteration speed ~35% via modularization and CI/regression checks.”

“Full-stack engineer with startup experience who owned end-to-end features on Impact’s Hiring Solutions platform, including a hiring inbox spanning React UI and Postgres data models; the product helped drive 500+ jobs filled shortly after launch. Comfortable designing modern React/TypeScript + Node architectures (GraphQL, testing, migrations) and operating on AWS (RDS, EC2/Fargate, S3, Datadog, CircleCI). Also founded their own startup (Bibbr) and made early-stage stack/infrastructure decisions under high ambiguity.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

“Built and operated a production TypeScript backend for a stateful conversational quoting chatbot at Tringapps, orchestrating multi-step workflows and session state while integrating with Salesforce and NetSuite. Implemented validation/retry logic, modular architecture, and strong logging/observability to handle real-world edge cases and external API failures.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”

“Built and productionized an internal LLM-powered document Q&A system at Morgan Stanley using a LangChain-based RAG pipeline (FAISS + OpenAI) with AWS ingestion (S3/Lambda), handling 100k+ pages and cutting lookup time ~35% while keeping responses under 3 seconds. Strong on reliability: automated evals/CI (pytest + GitHub Actions), CloudWatch monitoring, drift detection (prompt drift and fraud-model drift), and security controls (IAM + app-level authorization) in a financial-services environment.”

Pavan Kumar Malasani

Screened

Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI

Remote, USA4y exp

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

Venkata Surya Yashwanth Varma Vadlamudi

Screened

Junior Software Engineer specializing in distributed systems and full-stack web development

Ithaca, NY2y exp

“Software engineer at Cimpress owning end-to-end transactional pages for Pens.com (e-commerce). Built and integrated new discount experiences in a React/TypeScript + Node.js stack, focusing on modular component architecture to reduce tight coupling and avoid breaking existing functionality; prioritizes roadmap work using performance and conversion metrics.”

Dinesh Kumar Patibandla

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for finance and healthcare

Texas, USA4y exp

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

Hangquan Zhao

Screened

Senior Software Engineer specializing in connected vehicle platforms and real-time data systems

Mountain View, CA4y exp

“Open-source maintainer of KafkaJSUI, a Vue.js-based Kafka browser UI, focused on making large-topic exploration fast and responsive. Delivered major performance wins (incremental fetching, virtualized lists, WebSocket streaming, backpressure, Web Worker offloading) cutting load times to sub-200ms, and also strengthened CI and developer documentation while handling community-reported issues end-to-end.”

Pandari G

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems

San Francisco, USA5y exp

“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”

Khoa Nguyen

Screened

Junior Software Engineer specializing in Python, AWS, and data/ETL systems

Washington, USA1y exp

“Data/ETL-focused engineer with Amazon experience building and deploying AWS-based pipelines that became the primary source of automated customer feedback insights (processing millions of records daily). Demonstrated strong incident troubleshooting across software/host/network layers using CloudWatch, traces, and metrics, plus hands-on stakeholder and on-site operator collaboration to translate reporting needs into star-schema data models and tailored Python ETL logic.”

Sachin Reddy Kunta

Screened

Mid-Level Backend Software Engineer specializing in payments, fraud systems, and AI agent infrastructure

San Francisco, CA3y exp

“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”

“Built and shipped production LLM/RAG systems at Mayo Clinic, including a conversational AI assistant for patient pre-consultation and a clinical-trial matching tool for doctors. Implemented HIPAA-compliant de-identification and guardrails, plus real-time feedback logging and fine-tuning that improved response accuracy by 15% and reduced admin workload by 25%.”