Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, factory automation, IoT)
Background deploying ML models to edge devices or on-premises hardware in production environments
Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal
Familiarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server)
Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries
Experience building RAG systems, vector databases (Pinecone, Weaviate, Milvus), or LLM deployment pipelines
Background with computer vision model deployment, OCR/IDP systems, or document processing pipelines
Experience coordinating with platform or infrastructure teams at larger organizations (e.g., working with central ML platform teams)
Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration
Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices
Experience in regulated environments (NNPI/ITAR) and secure model/data governance
Experience in hyper-growth startup environments, with demonstrated success balancing speed, ambiguity, and long-term system health
Familiarity with enterprise systems such as ERP, MES, WMS, or PLM
Eligible to obtain and maintain a U.S. Secret security clearance
