ML Operations Bridge Engineer

We’re looking for a hands-on ML Operations Engineer who thrives at the intersection of data science and software engineering. In this role, you’ll take models from Jupyter notebooks to production APIs serving millions of predictions daily, shaping our MLOps infrastructure and having a massive impact on a fast-moving, ambitious team.What you’ll doDeploy ML models to platforms like Modal.com or Vertex AI with <100ms latency.Build and optimize real-time data pipelines (Kafka, Postgres/AlloyDB).Develop APIs in TypeScript/Python/Elixir to serve predictions at scale.Implement A/B testing, model versioning, and rollback strategies.Create monitoring dashboards to detect drift and ensure reliability.Collaborate with Data Scientists, Platform Engineers, and Product Teams to integrate ML features into core services.What we’re looking for5+ years in software/data engineering or MLOps roles.Strong coding skills in TypeScript, Python, or similar.Experience with ML deployment (Vertex AI, Modal, or similar).Knowledge of streaming platforms (Kafka, etc.) and CI/CD pipelines.Familiarity with SQL/NoSQL databases and feature store concepts.A production mindset: pragmatic, impact-driven, and self-directed.Bonus points forElixir or functional programming experience.Cloudflare Workers / edge computing.Kubernetes, container orchestration.Open-source ML/data contributions.Domain knowledge in logistics, e-commerce, or supply chain.