Vetted pytest Professionals

Andrew Fares

Screened ReferencesStrong rec.

Mid-level Data Engineer specializing in AI, GenAI, and cloud data platforms

Seattle, WA4y exp

“Built production AI systems inside AWS finance/procurement, including an LLM-based supplier quote classification and price-vetting workflow that drove $5M in savings over 3 months. Combines GenAI evaluation expertise, internal platform design, and reusable Python data-quality tooling with strong cross-functional execution across finance, accounting, and hardware engineering.”

“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”

“Early-career software engineer who owns revenue-critical invoice processing and internal ops tooling end-to-end. Has built TypeScript/React systems backed by MongoDB and Temporal, and designed scalable SQS-based onboarding workflows with FIFO/DLQ monitoring. Notably redesigned an Authzed SpiceDB authorization model, shrinking a 500+ line schema to ~20 lines while meeting sub-100ms p95 latency.”

“Player-coach engineering leader focused on AWS ML infrastructure and deep learning image delivery: provisioned EKS/Kubernetes for multi-node training and automated image release pipelines (Python + AWS CDK) to cut release time from 2 weeks to 1. Also built customer migration tooling for SageMaker HyperPod and owned a security incident end-to-end, implementing prevention tests and process improvements.”

Kaushik Sriram

Screened

Mid-level Software Engineer specializing in event-driven FinTech backend systems

San Francisco, CA5y exp

“Senior/Staff-level backend/platform engineer who owned Stripe’s global payout settlement system end-to-end, building an event-driven Python/Kafka platform processing millions of events daily across 30+ countries. Deep experience operating high-reliability distributed systems in production (incidents, replays/backfills, schema evolution, observability) and scaling on AWS/EKS with strong testing and deployment practices.”

“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.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”