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Vetted Amazon SQS Professionals
Pre-screened and vetted.
Senior Full-Stack Software Engineer specializing in scalable web and mobile applications
Senior Full-Stack Engineer specializing in AI-powered SaaS and cloud-native analytics
Staff Software Engineer specializing in FinTech platforms and distributed systems
Senior Software Engineer specializing in Python, APIs, and cloud-native integrations
Senior Software Engineer specializing in cloud-native healthcare platforms
Staff Platform Architect specializing in distributed systems and enterprise architecture
Staff Full-Stack Engineer specializing in data engineering and real-time event platforms
Senior Software Engineer specializing in cloud platforms, healthcare imaging, and scalable APIs
Senior Software Engineer specializing in healthcare imaging and FinTech systems
Mid-level Software Engineer specializing in backend, ML platforms, and FinTech
Senior Software Engineer specializing in FinTech payments and scalable platforms
Director-level Software Development Manager specializing in AWS infrastructure and distributed systems
Staff Software Engineer specializing in Healthcare SaaS and real-time systems
Mid-Level Software Development Engineer specializing in AWS serverless and ML/GenAI
Director-level Software Development Manager specializing in large-scale cloud platforms
Manaswini Gogineni
Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development
“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”
Jolie Qiu
Mid-Level Software Engineer specializing in AWS data infrastructure and pipeline automation
“AWS-focused software engineer who built a self-serve ETL pipeline scheduling service for non-engineers, including automated CloudFormation-based onboarding that cut setup time from 2–3 weeks to ~5 minutes. Strong in production reliability and customer-facing data platforms (EMR/DynamoDB/Lambda), with examples spanning pagination at scale, cross-table consistency, and phased rollouts to improve Parquet log SLAs.”
Bhavani Shekhawat
Engineering Manager specializing in AI/ML platforms and 0→1 product delivery
“Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.”
Sahil Sinha
Mid-Level Software Engineer specializing in full-stack development, cloud, and data infrastructure
“Software engineer at Fannie Mae (~3 years) working on high-volume loan data pipelines using AWS (SQS/S3), Java listeners, Postgres, and Python/SQL-based data quality validation. Also built a chess data collection system (leveraging experience as an International Master) with robust retry/monitoring, schema-change handling, and idempotent backfills to prevent bad data from reaching downstream systems.”
“Backend/full-stack engineer (Amazon experience) who built an AWS-based integration testing platform using Flask, ECS, Docker, and CloudWatch—cutting 1000+ test cases from ~5 hours to ~30 minutes while improving log visibility for non-engineering users. Also led a zero-downtime EU region migration with rigorous ORR testing, and built a Kinesis/Firehose/S3 + Glue/Spark replay mechanism for resilient data recovery. Side project: reproducible, cost-efficient LLM hosting platform on EKS using CDK and Karpenter for scale-to-zero.”
Kunj Amrutbhai Patel
Mid-level Software Engineer specializing in distributed backend systems on AWS
“Built production systems in the AWS ecosystem, including an internal AI assistant for diagnosing account transfer and permissions issues and an end-to-end account transfer workflow used by enterprise customers. Stands out for combining LLM/RAG design with strong distributed systems reliability practices, emphasizing guardrails, fallbacks, and operational trust in high-stakes workflows.”