Vetted MLOps Professionals

Brian Sanders

Screened

Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines

Chicago, IL12y exp

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

“Entrepreneurial builder and former founding CTO of a Y Combinator startup that was acquired. Now in the planning stage for a new venture (considering edtech, media creation, and food/"intelligent servicing"), emphasizing early design partners, rapid testing, and iteration to validate ideas.”

“LLMOps-focused technical leader who took an LLM use case from prototype to production for a non-technical customer by combining trust-building and structured enablement with a robust AWS/Kubernetes-based MLOps stack. Built observability and rollback mechanisms (Grafana + MLflow) to troubleshoot in real time, and scaled delivery by hiring a 5-person team while partnering with sales to manage expectations and drive adoption across departments.”

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Engineering/data platform leader from FLYR (airline ML forecasting and automated pricing) who built scalable ingestion/ETL and a canonical data model to onboard airlines with highly heterogeneous source systems. Created a golden-metrics layer for airline KPIs and implemented monitoring/backfill capabilities, cutting onboarding time by 50%+ while improving SLA performance and controlling cloud/ML training costs through stronger data quality gates.”