Machine Learning Engineer
Core
Own internal ML infrastructure, optimize real-time data pipelines, and scale system architecture for deep tech/biotech applications.
Role type
Senior MLOps / Machine Learning Platform Engineer
Builds
Enterprise MLOps ecosystem, low-latency microservices for sensor telemetry, production monitoring frameworks, internal developer tools and SDKs.
Domain
Deep Tech / Biotech / Industrial Analytics
Deliverable
production ML models | infrastructure
Required skills
Python, Go/Rust/C++/Java, Kubernetes, Helm, Docker, Databricks, Spark, Kafka/Redpanda/MQTT, time-series databases, microservices architecture, observability frameworks (Prometheus, Grafana), model deployment, container orchestration, low-latency optimization, SOLID principles.
Preferred skills
Master's or PhD in Intelligent Systems/Signal Processing, biomedical/biochemical/industrial IoT dataset experience, security expertise (API security, DevSecOps).
Technologies
Databricks, MLflow, Seldon, Triton, Prometheus, Grafana, Kubernetes, Helm, Docker, Kafka, Redpanda, MQTT, Spark.
Responsibilities
Scale enterprise MLOps ecosystem across the full model lifecycle; Build low-latency microservices for multivariate time-series sensor telemetry; Design custom pre/post-processing pipelines and optimize execution paths; Deploy production monitoring frameworks for drift detection and anomaly checks; Create internal tools and SDKs for data scientists to deploy and version models.
Seniority
Senior, hands-on IC
