Vetted AWS Lambda Professionals

“Data engineer who has owned end-to-end ELT pipelines on Airflow + AWS (S3/Glue/Lambda) with Snowflake/Redshift, processing millions of records per day and tens of GBs via PySpark. Built strong data quality and reliability practices (40% quality improvement, 99%+ uptime), and also designed a resilient web-scraping system with anti-bot defenses and schema-change versioning plus REST APIs for serving curated data.”

“Built production-style backend and AI systems across internship and project work, including a real-time sports platform backend and a Smart Email Assistant using GPT-4. Stands out for combining classic backend performance engineering with practical LLM workflow design, including measurable latency improvements, high uptime, and debugging of non-deterministic model behavior.”

Tejaswi devarapalli

Screened

Junior Software Engineer specializing in backend systems and AI infrastructure

Los Angeles, CA2y exp

“Built both a full-stack AWS file-processing pipeline and a production AI document Q&A system ('smart-doc'). Stands out for combining strong cloud engineering with practical LLM/RAG architecture, including hybrid retrieval, reranking, structured outputs, confidence-based retries, and production monitoring.”

“Fullstack engineer with a strong serverless/AWS and applied AI profile, having built document-upload-to-RAG chatbot systems using Bedrock/Titan as well as a production fintech reporting platform. Particularly compelling for teams needing a zero-to-one builder who can own architecture, frontend, backend, and user-informed product delivery; one recent product reached 100+ student users within 48 hours of launch.”

“Full-stack engineer with experience delivering an end-to-end NIH application using Angular and scalable .NET Core microservices backed by MySQL. Has hands-on depth in complex approval/workflow implementations (RBAC + workflow engine) and performance tuning for million-record, data-driven systems, plus 5 years with Java/Spring Boot microservices and React.”

“Application security engineer/advisor with hands-on experience securing AWS-based, containerized services and embedding SAST/DAST/SCA and container scanning into GitHub/GitLab CI/CD. Drove measurable outcomes (50% faster vuln triage, 40% fewer misconfigs) and has deep operational troubleshooting experience in Kubernetes (agent failures due to CPU throttling/network policies), plus pragmatic strategies to reduce developer friction and handle API rate limits.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”

“Full-stack engineer working on a medical practice SaaS, who owned an end-to-end charge entry module handling patient charges/payment workflows and complex client-side data transformations. Also built a fintech rating platform with real-time WebSocket streaming and TradingView-based charting, and has 10 years of university experience mentoring student "startup" capstone projects under tight deadlines.”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

“Full-stack engineer who was the sole cross-stack builder on a marketplace serving 400,000+ users, owning everything from React dashboards and core APIs to AWS infrastructure and scaling improvements. Stands out for combining strong technical depth with product instinct—shipping a payment-on-delivery feature in a week that increased sales 5x, and proactively building AI-powered internal tooling to eliminate manual recruiting workflows.”

“Early-career full-stack engineer who has not yet shipped a professional customer-facing product but has built sophisticated AI-driven systems in personal/academic work, including a multi-tenant AI knowledge base (async ingestion, pgvector semantic search, SSE real-time updates, knowledge graphs) and an AI-powered code review assistant designed to process thousands of jobs via Redis/BullMQ.”