Vetted GitHub Actions Professionals

“Backend owner at Apple for an AI workflow orchestration service, with hands-on experience stabilizing peak-traffic production systems using OpenTelemetry-style tracing, bounded async concurrency, and database performance tuning. Built and shipped a Python LLM-agent orchestration layer to automate multi-step operational workflows, emphasizing guardrails, auditability, and deterministic fallbacks to keep non-deterministic AI behavior production-safe.”

“Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.”

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

“Backend engineer with healthcare platform experience at Thirty Madison, combining Django for secure, data-heavy core services with FastAPI async microservices for real-time patient monitoring. Led Kubernetes migration with Istio service mesh, autoscaling (HPA), and stateful storage patterns, and implemented GitOps CI/CD using ArgoCD. Also built real-time Kafka streaming pipelines with reliability patterns like idempotent producers and offset management.”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”

“Backend/data engineer with production experience at Walmart and HealthSnap building Python services and data pipelines on AWS (EKS, Lambda, Glue, Airflow). Strong reliability and operations focus—implemented idempotency + circuit breakers for peak-traffic consistency issues, GitOps CI/CD, and observability. Demonstrated measurable performance wins (Postgres p95 45s to <5s, ~60% CPU reduction) and modernized SAS batch workflows to Python with parallel-run parity validation and feature-flagged rollout.”

“Built and shipped a production LLM-driven multi-agent platform (ciATHENA) at CustomerInsights.AI to automate analytics/ML/compliance workflows in healthcare and life sciences. Implemented LangGraph/LangChain orchestration with strong backend-style rigor (schemas, Pydantic validation, retries, auditability) and optimized latency/cost while keeping the system usable for non-technical users via guided natural-language interactions and structured/visual outputs.”

“Backend/data engineer who has owned production Python APIs and high-throughput async workflows on AWS (FastAPI, Docker, ECS/EKS/Lambda) with mature reliability practices like idempotency, bounded retries, circuit breakers, and strong observability. Also built AWS Glue ETL into an S3/Redshift lakehouse and modernized legacy batch systems via parallel-run parity testing and feature-flagged migrations, including a SQL tuning win cutting a multi-minute query to under 10 seconds.”

“Backend engineer who built an event-driven, multi-service resume review system integrating AI/ML workflows. Demonstrated strong performance engineering (e.g., composite indexing dropping latency from ~600ms to ~35ms and major P95 gains) and high-throughput pipeline optimization via caching, batching, and worker concurrency tuning, with multi-tenant isolation implemented across DB and Redis.”