MLOps Engineer
Core
Build scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks for a Large Tabular Model (LTM) to enable enterprise decision-making.
Role type
Senior MLOps Engineer
Builds
Production-ready model serving infrastructure, inference architectures, feature stores, and data pipelines for a flagship LTM.
Domain
Enterprise AI / Large Tabular Models / Cloud Infrastructure
Deliverable
production ML models
Required skills
MLOps platforms (MLflow, WandB), ML frameworks (PyTorch, TensorFlow), model serving (TorchServe, Triton, KServe), Kubernetes, Infrastructure as Code (Terraform, Helm, GitOps), Python, Bash, Go, observability (Prometheus, Grafana, Datadog, OpenTelemetry), AI/ML security and governance.
Preferred skills
ML workflow tooling (Kubeflow), FastAPI, data platforms (Databricks, Snowflake), SRE practices, cloud security certifications.
Technologies
TorchServe, TensorFlow, Triton, KServe, Kubernetes, Terraform, Helm, GitOps, Prometheus, Grafana, Datadog, OpenTelemetry, MLflow, WandB, PyTorch, TensorFlow, Python, Bash, Go, FastAPI, Databricks, Snowflake.
Responsibilities
Develop and manage scalable, automated machine learning pipelines and CI/CD workflows; Design and implement robust model serving infrastructure; Develop scalable inference architectures optimized for ultra-low latency and high throughput; Ensure seamless model deployment via A/B testing, canary releases, and rollback; Develop logging, alerting, and monitoring solutions; Improve GPU usage and streamline resource allocation; Design, implement, and maintain feature stores and scalable storage solutions.
Seniority
Senior, hands-on IC
