Vetted Model Monitoring Professionals

“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”

Jeremiah Medina

Screened

Senior AI/ML Engineer specializing in LLMs, RAG, and cloud-native MLOps

Orlando, FL11y exp

“Built and owned a real-time clinical AI assistant at Andor Health, taking it from prototype through deployment, monitoring, and iterative improvement. Brings strong healthcare-focused GenAI experience across RAG, hybrid retrieval, LoRA fine-tuning, and production Python services, with measurable gains in accuracy, speed, and reliability.”

“IAM/platform engineer with experience at DocuSign and Siemens who ships production-grade systems end-to-end: built a secure AWS serverless internal employee-profile API (OAuth2/Cognito/WAF) that cut data retrieval from weeks to near-instant and sustained ~2,800 RPS at ~75 ms. Also delivered production AI workflows, including a GPT-4o + Playwright crypto-scam detection agent and an NLP ticket-routing system improved to ~86.7% accuracy with strong monitoring and incident mitigation practices.”

“Unity/C# gameplay engineer with strong systems architecture depth who has reworked core gameplay ability frameworks, shipped across mobile and standalone VR, and solved multiplayer synchronization issues with server-authoritative netcode. Also brings an unusual crossover into AI tooling, having owned an AI-powered debugging assistant at Arm and integrated LLM workflows into CI/development pipelines.”

“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.”

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.”

“Atlassian ML/AI engineer who has shipped end-to-end production systems combining classical ML, streaming infrastructure, and LLM-based personalization to improve onboarding and free-to-paid conversion. Particularly strong in turning research-style RAG and reranking ideas into low-latency, reliable product systems with robust evaluation, safety guardrails, and reusable platform services for other teams.”

“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.”

Durgaprasad G

Screened

Mid-level AI/ML Engineer specializing in LLM infrastructure, RAG, and agentic systems

New York City, NY3y exp

“Stripe engineer who owned and unified multiple team RAG systems into a shared production platform used by 200+ internal operators, deployed on EKS with Kafka ingestion and hybrid retrieval. Drove measurable business outcomes including <400ms latency, ~35% inference cost reduction, ~25% accuracy lift via fine-tuning, and real-time auto-approval of 80%+ merchant compliance applications through strong observability and reliability patterns.”