Vetted Unit Testing Professionals

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

Zahaan Khan

Screened

Mid-Level Full-Stack Software Engineer specializing in ads transparency platforms

San Jose, USA6y exp

“TikTok engineer (4 years) who built and owned multiple ad transparency platforms used by internal teams and advertisers worldwide. Strong full-stack profile spanning Next.js/TypeScript + Redux frontends, a secure/optimized BFF layer, and event-driven backend workflows (Kafka, retries/DLQ, Redis idempotency) with heavy emphasis on observability and performance; cites a 20% moderation efficiency gain on a modernized legacy tool.”

PoHung Chen

Screened

Junior AI/ML Engineer specializing in MLOps and real-time model serving

New York, NY2y exp

“Software engineer with Amazon experience who has built LLM-powered and hybrid ML systems for ad auction/relevance at massive scale. Most notably, they described redesigning brand-query classification with a GPT-4-assisted offline cache plus fallback architecture that improved accuracy from 72% to 99%, reduced latency and costs, and was credited with an estimated $130M revenue lift.”

Patrick Jose

Screened

Senior AI Engineer specializing in LLM applications and full-stack systems

San Francisco, CA8y exp

“Built and owned a production LLM/RAG customer support assistant end-to-end, from prototype through deployment, monitoring, and iteration. Their work automated roughly 40% of common support queries and cut response times by about 30%, while also creating reusable Python inference services that improved consistency and team velocity.”

Timothy Lee

Screened

Senior Full-Stack Engineer specializing in AI platforms and scalable web systems

Live Oak, FL11y exp

“Built and shipped production agentic/LLM systems that could safely perform real customer and subscription operations, not just answer questions. Demonstrates unusually strong depth in agent orchestration, tool safety, evals, tracing, and backend workflow design across Node.js/TypeScript, Go, Redis, Postgres, Kafka, and GPT-4.”

“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

“Built and owned the end-to-end architecture for a Quick Flows “research card” backend at AWS, using an event-driven AWS stack (SNS/SQS, DynamoDB, S3) to support asynchronous research output processing and status tracking. Emphasized maintainability via unit tests, smoke tests, and CI/CD with staged environments (devtest and gamma).”

“Unity/C# gameplay engineer who has shipped a modular, data-driven combat ability system with strong measurable outcomes (≈80% fewer GC allocations, 15–20% better frame times, 10–12% higher early retention). Also integrated an LLM-driven NPC dialogue/quest hint system with a C#/.NET backend, caching/guardrails, and telemetry-driven iteration, and shipped Photon PUN real-time 4-player co-op plus a shared codebase across Meta Quest VR and iOS/Android.”

“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).”