• Set technical strategy and drive a multi-quarter roadmap for ML platform capabilities aligned to Shippo’s business priorities. • Own cross-team architecture decisions, RFCs, and design reviews for ML lifecycle and inference. • Raise the engineering bar through mentorship, production readiness standards, and reusable platform primitives. • Be accountable for platform adoption, reliability, and cost-performance outcomes. • Build and operate core ML platform components: ML lifecycle foundation (experiment tracking, reproducibility, artifact management, model registry, versioning, and controlled promotion workflows using MLflow or equivalent). • Training and experimentation enablement (standardized environments, reusable pipelines/templates, evaluation harnesses, and repeatable workflows that let data scientists move from exploration to production with confidence). • Kubernetes-native model serving for real-time inference (safe rollout and rollback, autoscaling, reliability practices, and cost controls). • Batch inference and scoring pipelines (repeatable backfills, retraining triggers, consistent packaging between training and inference). • Observability for ML systems (service health metrics, alerting, and model-quality signals such as drift and data quality). • Developer experience (templates, reference implementations, documentation, and self-service workflows). • Evaluate and recommend inference frameworks and deployment patterns, and document tradeoffs for Shippo’s workloads. • Identify and resolve performance bottlenecks across the inference stack (model runtime, compute utilization, networking, serialization, and autoscaling behavior). • Establish ML engineering standards across training, evaluation, testing, model packaging, CI/CD, production readiness, and incident response. • Partner with Data Science teams to bridge research and production environments by creating repeatable frameworks, shared standards for code quality and reproducibility, and self-serve paths to deploy models safely. • Collaborate with Data and Engineering teams to ensure the platform supports real workflows, drives adoption, and meets reliability expectations. • Mentor engineers through design reviews, architecture guidance, and shared best practices across platform and ML development.