Machine learning engineer

Senior MLOps / Machine Learning Platform Engineer Location: Hybrid / Remote Options Industry: Deep Tech / Biotech / Industrial Analytics Employment Type: Full-time The Role We are seeking a Senior MLOps / ML Platform Engineer to own our internal ML infrastructure, optimize real-time data pipelines, and scale our system architecture. If you treat model deployment, observability, and container orchestration as a rigorous engineering discipline rather than relying on AI shortcuts, let's talk. Core Responsibilities Platform Architecture: Scale our enterprise MLOps ecosystem (Databricks, MLflow, Seldon, or Triton) across the full model lifecycle. Backend Engineering: Build low-latency microservices capable of handling massive streams of multivariate time-series sensor telemetry. Inference Optimization: Design custom pre/post-processing pipelines and optimize execution paths for low latency. Observability: Deploy production monitoring frameworks (Prometheus, Grafana) for drift detection and anomaly checks. Developer Experience: Create internal tools and SDKs to let data scientists deploy and version models seamlessly. What We Are Looking For Experience: 5+ years shipping production-grade backend systems and operationalizing applied ML models at scale. The Stack: Advanced Python coupled with production mastery of a lower-level language (Go, Rust, C++, or Java). Infrastructure: Deep experience with Kubernetes, Helm, Docker, and cloud data platforms (Databricks, Spark). Data Streams: Familiarity with message brokers (Kafka, Redpanda, MQTT) and time-series or distributed databases. First-Principles Mindset: Focus on clean/SOLID code and independent, deterministic engineering over AI shortcuts. Nice-to-Haves Master's or Ph D in Intelligent Systems, Spatio-Temporal Data, Signal Processing, or Distributed Computing. Experience handling biomedical, biochemical, or industrial Io T sensor datasets. Security expertise (API security, vulnerability mapping, or Dev Sec Ops compliance)