Vetted Amazon SNS Professionals

“Backend-focused engineer with deep Java/Spring expertise in fintech and SaaS integrations, including high-scale financial data pipelines and partner-facing APIs. Most notably re-platformed a 100M+ record ETL system to a custom concurrent Spring Batch architecture that cut failures dramatically and reduced infrastructure costs by over 90%, while also leading enterprise-grade event-driven integrations for customers like Bosch and Amazon.”

“Software engineering candidate who built a compiler-like Python tool to translate between Python code and UML-style diagrams (and back). Also has hands-on AWS experience building a distributed pub/sub system using services like Lambda, API Gateway, ELB, WAF, VPC, and DynamoDB, plus ML projects using Kaggle datasets (e.g., diabetes risk analysis).”

“Infrastructure/Unix engineer with production PowerHA/HACMP operations experience (resource groups, service IPs, shared storage) who has executed planned failovers and recovered a real outage involving a SAN driver crash and manual Oracle recovery (restored service in ~15 minutes with zero data loss). Also supports cloud DevOps practices including CI/CD security scanning (SonarQube, Snyk), container registry/versioning, and Terraform Cloud-based IaC across AWS and GCP with PR/Jenkins-driven plan-and-apply workflows.”

“Data engineer/backend engineer who owns large-scale, real-time event pipelines on AWS end-to-end, including a petabyte-scale CDC ingestion flow from multiple Postgres DBs into Redshift. Re-architected a legacy DynamoDB+S3 approach into a Delta Lake + DuckDB/PyArrow-compatible design, improving performance dramatically (e.g., ~600s to ~10s for 1k records) and increasing reliability at high file volumes.”

Advitha Bawgi

Screened

Junior Full-Stack Software Engineer specializing in cloud-native microservices

India1y exp

“Backend engineer with hands-on IoT and AI product work: built a decoupled Raspberry Pi + AWS IoT Core weather monitoring backend and a Dockerized FastAPI LLM service on AWS ECS using OpenAI/HuggingFace with an emerging RAG layer. Also delivered measurable performance gains at DAZN by redesigning event-driven/serverless ingestion (SNS, S3->Lambda->DynamoDB), cutting latency ~30% and boosting throughput ~25% while automating ~90% of manual sync work.”

Swathi Sankaran

Screened

Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI

New York, NY10y exp

“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”

“Backend engineer with hands-on experience building event-driven Java/Spring Boot and Kafka systems, plus AI-assisted document-classification workflows in enterprise environments. Stands out for a thoughtful, risk-aware approach to AI: uses it to accelerate delivery, but emphasizes validation layers, confidence thresholds, observability, and human review before AI can affect downstream business actions.”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“Backend engineer focused on Python/FastAPI microservices, with hands-on experience deploying to AWS (EKS/ECR) via Jenkins and GitOps-style workflows using ArgoCD. Has built and stabilized real-time Kafka payment-event streaming pipelines and improved production performance under peak load through Redis caching, SQL optimization, and robust retry/DLQ patterns. Also supported phased migrations from on-prem environments to AWS with gradual traffic shifting and monitoring.”

“Uber engineer who has owned internal products end-to-end across backend (Spring Boot microservices, MySQL) and frontend (React), including performance optimization and secure JWT-based auth. Also shipped a production internal RAG/embeddings LLM support assistant over policy docs and support tickets, with guardrails (confidence thresholds, human review) and an evaluation loop that directly reduced hallucinations.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

“Built and shipped AI-powered fraud investigation and risk dashboard features at Barclays, combining full-stack engineering with RAG-based LLM systems in a compliance-sensitive environment. Stands out for pairing explainable AI architecture with measurable business impact: improving fraud detection accuracy by 18% while reducing analyst investigation time by 14%.”

“AIX/IBM Power Systems engineer with hands-on production incident leadership in a regulated banking environment, using deep OS-level tooling to diagnose CPU entitlement and memory pressure issues. Experienced with HMC/vHMC, VIOS, and zero-downtime DLPAR resizing, plus PowerHA/HACMP clustering and validated failover testing. Also drives migration readiness via Bash/Python automation (60% manual-effort reduction) and phased AIX cloud/hybrid cutovers.”

“Software engineer/product-focused builder who has delivered customer-facing dashboards (React/TypeScript + Spring Boot) and microservices using RabbitMQ, emphasizing safe, fast iteration with CI/CD, feature flags, and monitoring. Also built an internal monitoring/reporting tool adopted by ops/support by involving users early and iterating based on feedback.”

“Built and owned major parts of a real-time distributed AI fraud-detection pipeline (ingestion, inference microservice integration, and automated action layer), optimizing latency and observability and reducing false positives by ~35%. Understands ROS/ROS2 concepts (nodes/topics/services) and planned hands-on ramp-up via ROS2 pub/sub exercises and Gazebo simulation, but has not worked on physical robots or ROS in production.”

“Backend engineer with strong AWS/CI/CD experience (multi-repo deployments, Lambda + core app, immutable ECR and image promotion) and a published master’s thesis building an ML framework for Solar PV energy prediction and CO2 reduction impact modeling using ensemble and meta-learning approaches benchmarked against SAM.”

“ML/NLP practitioner with experience building a transformer-failure prediction system that combines sensor signals with unstructured maintenance comments using LLM-based extraction and similarity validation. Strong emphasis on production readiness—data leakage controls, SQL-driven data quality tiers, and rigorous bias/fairness validation (including contract/spec evaluation across diverse company profiles).”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”