Vetted AWS CodePipeline Professionals

“Player-coach engineering leader focused on AWS ML infrastructure and deep learning image delivery: provisioned EKS/Kubernetes for multi-node training and automated image release pipelines (Python + AWS CDK) to cut release time from 2 weeks to 1. Also built customer migration tooling for SageMaker HyperPod and owned a security incident end-to-end, implementing prevention tests and process improvements.”

“Unity/C# gameplay engineer with strong systems architecture depth who has reworked core gameplay ability frameworks, shipped across mobile and standalone VR, and solved multiplayer synchronization issues with server-authoritative netcode. Also brings an unusual crossover into AI tooling, having owned an AI-powered debugging assistant at Arm and integrated LLM workflows into CI/development pipelines.”

“Robotics software engineer with hands-on ROS2 experience building an audio conversion node and integrating Whisper LiveKit for streaming speech-to-text in a simulated hostile (outer space) robot environment. Also worked on a 2023 LiDAR + ML vision obstacle-detection project for a hospital-nurse-assistant robot, and has strong large-scale CI/CD deployment experience from AWS (2022–2024) across alpha/pre-prod/prod stages.”

“Amazon Prime Video backend engineer who built and operated high-traffic Python/FastAPI services and AWS-native data/batch systems. Demonstrates strong production reliability and incident ownership (CloudWatch/X-Ray), plus measurable performance wins (8s to <200ms query latency, ~40% CPU reduction) and cost-focused architectures (Lambda + ECS/Fargate with Fargate Spot).”

“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.”

“Built and owned end-to-end deployment and AI support workflows spanning CI/CD, Kubernetes, Terraform, and LLM/RAG systems. Stands out for combining DevOps delivery with production AI operations, including secure tool-calling, incident debugging, retrieval quality controls, and validation-first document ingestion for messy real-world inputs.”

Pavanika Thotakura

Screened

Senior Data Engineer specializing in cloud big data pipelines and real-time streaming

Seattle, WA6y exp

“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

Yeshwanth Sai Pala

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech

Remote, USA4y exp

“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”

KARTHIKBABU VADLOORI

Screened

Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices

San Francisco, CA5y exp

“Backend engineer with experience at Meta and Accenture building regulated-data systems (healthcare/financial) using Python/Flask and Postgres. Has scaled high-throughput services to millions of daily requests, delivering measurable latency wins (~40% API latency reduction; ~35% faster DB-backed endpoints), and has productionized ML inference services using Docker/Kubernetes and AWS (ECS/SageMaker).”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”