Vetted Amazon S3 Professionals

“LLM/agentic systems practitioner who partners directly with customers to productionize prototypes end-to-end—defining business-aligned metrics, building evaluation datasets, and shipping monitored, cost-bounded inference APIs on AWS Lambda. Notably delivered a vehicle damage classification system that cut manual review by 40% and stabilized agent workflows by instrumenting state transitions to uncover and fix a race-condition-driven skipped tool call.”

“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”

“Backend/data engineer with production experience at Uber building a near real-time driver rewards service on AWS (FastAPI, PostgreSQL, Redis) with strong reliability and concurrency controls. Also delivered AWS Lambda/ECS containerized deployments with GitHub Actions CI/CD and cost governance, built AWS Glue ETL with schema-evolution handling, and drove a ~10x SQL performance improvement while owning incident response via CloudWatch.”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

Chang Qian

Screened

Senior Backend/Infrastructure Engineer specializing in large-scale integrity and content systems

Menlo Park, CA10y exp

“Backend/platform engineer who built Bilibili’s "Avalon" content moderation platform from a vague CEO mandate into a company-wide service (Go, gRPC, Kafka), including on-call, metrics, transparency tools, and multi-site resiliency work. More recently at Meta, scaled a high-traffic mistake-prevention platform by introducing capacity levers (prefiltering, caching, log sampling, fanout limits) and navigating org-wide constraints, including debugging a rule-engine threading bottleneck.”

Sahitya Thota

Screened

Staff/Tech Lead Software Engineer specializing in identity, data platforms, and cloud systems

Fremont, CA20y exp

“Engineering leader/player-coach who built and owned end-to-end sales-facing data products, including a 360° advertiser insights platform and a sales AI agent for natural-language access to insights. Demonstrated strong architecture and reliability chops (event-driven redesign to eliminate Hive-query throttling; batching/caching to reduce fan-out) plus incident ownership around ads attribution consistency. Also has 0→1 experience building graph-based recommendation/matching systems with explainability and tight user feedback loops.”

“Backend/full-stack engineer with experience building large-scale systems on Salesforce and eBay, including a sharded Oracle-backed personalization store used by millions of sellers and a Spring Boot microservice that executes scheduled payouts at high volume. Emphasizes production readiness via state-machine-based idempotency, strong observability (Splunk/Elasticsearch/PagerDuty), and extensive Selenium/Sauce Labs automation.”

Chavez Yi Wei Cheong

Screened

Mid-level analyst and software engineer specializing in AI, distributed systems, and product delivery

San Francisco, CA5y exp

“Engineer with end-to-end shipping experience spanning large-scale infrastructure at Meta and scrappy startup automation work. Built a C++ streaming platform for Facebook Marketplace with canary and chaos testing, and also created Selenium-based scraping and GTM automation workflows under ambiguous, real-world conditions.”

“Backend/AI engineer currently at Stripe building Minions, an internal LLM-based developer agent that automates code generation, bug fixes, and PR creation. They combine strong production LLM architecture skills with reliability engineering, having improved routine PR merge times by ~40-45%, lowered post-merge bug rates by 12%, and previously built an invoice-processing pipeline that achieved 91% straight-through processing in a B2B payments context.”