Data Scientist - ML Engineering
Core
Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
Role type
Senior IC MLOps engineer (ML infrastructure)
Builds
Forecasting engines, optimization solvers, NLP models, CI/CD workflows, model registry, drift monitoring systems
Domain
Enterprise-scale ML systems, multi-cloud infrastructure
Deliverable
production ML models
Required skills
Spark, Azure Databricks, MLflow, Kubernetes, Docker, CI/CD, model registry, versioning, lineage, audit compliance, model-drift monitoring, retraining automation, containerization, observability
Preferred skills
Leadership in ML platform or DevOps teams, feature stores, feature engineering, AutoML, H2O
Technologies
Azure Pipelines, MLflow, Databricks, Kubernetes, Docker, Spark, H2O
Responsibilities
Architect end-to-end ML infrastructure; Lead deployment of forecasting engines, optimization solvers, and NLP models; Design CI/CD workflows; Implement model registry and audit compliance; Build model-drift monitoring and retraining automation; Mentor MLOps engineers; Drive adoption of MLOps practices.
Seniority
Senior, hands-on IC with mentorship responsibilities
