The Role
We are seeking a Senior MLOps / ML Platform Engineer to bridge the gap between AI research and production system architecture. You will own our internal ML infrastructure, optimize real-time data pipelines, and eliminate platform friction for our Data Science teams. If you treat model deployment, observability, and container orchestration as a rigorous engineering discipline, let's talk.
Core Responsibilitie
- sPlatform Architecture: Scale our enterprise MLOps ecosystem using Databricks, MLflow, Seldon Core, or Triton to manage the full model lifecycle
- .Backend Engineering: Build robust, low-latency microservices capable of handling massive streams of multivariate time-series sensor telemetry
- .Inference Optimization: Build custom pre/post-processing pipelines and optimize execution paths for low latency
- .Observability: Implement production monitoring frameworks (Prometheus, Grafana) for drift detection and anomaly checks
- .Developer Experience: Create internal tools and SDKs to let data scientists register and version models seamlessly
.
What We Are Looking F
- orExperience: 5+ years shipping production-grade backend systems and operationalizing applied ML models at scal
- e.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 database
- s.First-Principles Mindset: Focus on clean/SOLID code and independent, deterministic engineering over AI shortcut
s.
Nice-to-Ha
- vesMaster's/PhD in Intelligent Systems, Spatio-Temporal Data, Signal Processing, or Distributed Computi
- ng.Experience handling biomedical, biochemical, or industrial IoT sensor datase
- ts.Security expertise (API security, vulnerability mapping, or DevSecOps complianc
e).