Vetted Amazon EMR Professionals

Kaustubh Rai

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in distributed systems and cloud-native microservices

PA, USA2y exp

“Software engineer with ~2 years at UnitedHealth Group plus CMU coursework/TA experience, spanning backend modernization and cloud-native operations. Worked on migrating a customized open-source EDI system from Python 2 to Python 3 while improving SQLite database traceability via JSON exports, and has hands-on Kubernetes microservices deployment on Azure using Helm, HPA, and Jenkins-based Git-triggered CI/CD. Also built a large-scale real-time ride-hailing simulation using Kafka + Samza with explicit fault-tolerance strategies.”

“Director of Engineering at Proofpoint for 8 years, leading architecture and integration of Java microservices within a detection platform. Demonstrates pragmatic delivery leadership—incurring short-term cost to meet launch deadlines, then systematically paying down technical debt and optimizing AWS spend—plus a disciplined, long-horizon approach to backward-compatible API/schema evolution across many dependent services.”

Deepa Karunakaran

Screened

Senior Customer Success & Cloud Migration Leader specializing in AWS data and analytics

Cupertino, CA20y exp

“AWS enterprise customer leader supporting the Apple Maps account, focused on large-scale migrations from on-prem (e.g., Cloudera) to AWS EMR/EBS. Known for deep troubleshooting and cross-functional execution (Grafana-driven autoscaling fixes, Graviton capacity/ICE mitigation) and for tying roadmap alignment to commercial outcomes, including a cited $30M incremental ARR expansion.”

“Engineering leader (Director of Development) with hands-on cloud and product experience who builds business-aligned technology roadmaps and scales teams. Delivered an enterprise cloud-migration enabler at UHG by implementing AD authentication and Terraform-based IaC for custom VM images while meeting 90-day InfoSec patch/rotation requirements, and drove a 20% lift in user consumption/retention by designing an interactive branded media portal experience for Sunkist.”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

PoHung Chen

Screened

Junior AI/ML Engineer specializing in MLOps and real-time model serving

New York, NY2y exp

“Software engineer with Amazon experience who has built LLM-powered and hybrid ML systems for ad auction/relevance at massive scale. Most notably, they described redesigning brand-query classification with a GPT-4-assisted offline cache plus fallback architecture that improved accuracy from 72% to 99%, reduced latency and costs, and was credited with an estimated $130M revenue lift.”

“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”

“Built and shipped production AI systems spanning customer support automation at Uber, privacy-preserving federated health modeling on iOS, and an open-source semantic search layer for Postgres. Stands out for combining strong LLM/product instincts with rigorous eval design, measurable production impact, and zero-to-one execution across backend, mobile, and developer infrastructure.”