Vetted ETL Professionals

“AI/LLM practitioner who built a GPT-2-like language model from scratch at the University of Maryland using PyTorch and multi-GPU distributed training, with experiment tracking in Weights & Biases. As an AI Operations intern at ScaleUp360, delivered multiple production-style AI agent automations (Gmail classification and Fireflies-to-Claude workflows that extract and assign CEO tasks) and set up measurable evaluation using test cases and classification metrics.”

“Full-stack engineer with experience at Zoho and Amazon who has owned production systems end-to-end, including a monolith-to-microservices migration using Kafka and Cassandra that improved search latency ~25% and increased throughput without data loss. Also built a hackathon project (Buildwise) into a sold product for a construction company (AI-driven document compliance checks) and shipped an IoT-based parking availability MVP in 3 weeks.”

“Execution-focused technology leader and consultant who architects and implements core business systems (ERP/GL, Azure/M365, Google Workspace) and infrastructure to help organizations scale. Has supported growth from 0 to 55M and operated in a 250M+ environment, including standardizing hardware and deploying Intune/Jamf MDM to enable 10+ new retail store openings with consistent POS infrastructure.”

“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”

“Backend engineer who built and iterated a secure, multi-tenant RAG system over a large document corpus, emphasizing strict RBAC/ACL isolation, hybrid retrieval (vector+keyword), reranking, and strong observability to balance relevance, latency, and cost. Also led production refactors/migrations using strangler + feature flags/dual writes and has experience catching subtle real-world failure modes (including in a sensor calibration optimization pipeline).”

“Backend/data engineer with hands-on production experience across Python/FastAPI services and AWS (Lambda, API Gateway, SQS, ECS) delivered via Terraform and GitHub Actions. Built Glue-to-Redshift ETL pipelines with Step Functions retry/catch patterns, schema evolution safeguards, and data quality checks; also modernized a legacy SAS monthly reporting system into Python microservices with rigorous side-by-side parity validation. Demonstrated strong SQL tuning skills with a reported improvement from 5 minutes to 15 seconds.”

“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”

“Qualcomm engineer building Android applications that run on Qualcomm AI accelerators, with hands-on experience in C++ concurrency, chipset stress testing, and power/performance tuning. Has deployed on-device AI models and built deployment/log post-processing workflows using Docker/Kubernetes and CI/CD; interested in translating this embedded AI/performance background into robotics (perception/real-time systems).”

“SRE-focused engineer with experience at Sony Interactive Entertainment productionizing high-throughput LLM/agentic systems on Kubernetes, including GPU-aware autoscaling and warm-pool strategies to manage latency and cost under traffic spikes. Demonstrates strong incident response using Prometheus/Grafana + Jaeger tracing (e.g., resolving recursive agent loops and restoring 99.9% availability within minutes) and partners closely with sales/customer teams through PoV demos and developer workshops.”

“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”

“Technology leader who drove end-to-end modernization at Dogtopia—building a proprietary SaaS POS/CRM and operations platform plus an AI-powered customer app—using OKR-driven roadmaps and Agile/DevOps delivery. Previously at GE, led a cloud-native AWS data fabric re-architecture with strong security/governance (RBAC, classification, encryption, lineage, virtualization), cutting processing time 60%+ and enabling AI workloads tied to $400M in business value.”

“LLM infrastructure engineer who built a production-critical real-time personalization and memory retrieval system for a user-facing product, adding <100ms P99 latency while improving relevance ~20–25% and holding SLA through 3x traffic. Experienced designing tiered retrieval backends (Redis + vector store), deploying on Kubernetes with autoscaling/circuit breakers, and running rigorous observability, incident response, and agent evaluation (shadow traffic, A/B tests, regression/replay).”

“Data engineer at Plaid who built greenfield, end-to-end real-time transaction pipelines and FastAPI data services for fraud detection and analytics, handling millions of events per day. Strong focus on reliability and data integrity via Great Expectations validation, Airflow-based monitoring/SLAs, quarantine/staging patterns, and robust external data ingestion with schema versioning and backfills (reported 50% fewer anomalies and ~40% fewer failures).”

“Machine learning engineer and startup veteran building InfraSketch (infrasketch.net), a full-stack system-design/diagramming product where users describe systems in plain English and an LLM agent generates and iterates on infrastructure graphs and exports design docs. Owns the entire stack (React/TS + FastAPI/Node, DynamoDB/Postgres, AWS serverless) and focuses on LLM consistency, modular agent architecture, and production-style CI/CD and reliability patterns.”

“Backend-focused engineer with hands-on experience modernizing a legacy SNMP/PNM data collection system at CableLabs into a cloud-accessible Kubernetes pipeline, feeding Prometheus-formatted metrics into VictoriaMetrics and visualizing real-time network health in Grafana for 100+ modems. Also built a FastAPI + Supabase appointment booking portal for a clinic with encryption and phone-number-based auth, and has frontend experience debugging S3-based HEIF image rendering issues.”

“Cloud/platform engineering lead with deep Azure AKS and SRE/observability expertise who migrated an EY enterprise SaaS platform from monolith to cloud-native microservices, supporting 10+ products and ~$200M annual revenue with ~99.9% uptime. Also building an open-source Kubernetes-native AI agent orchestration platform (AgScale) in Go with CRDs/controllers, policy/tool governance, token budgets, and production-grade monitoring.”