Data Scientist - ML Engineering
Core
Architect end-to-end ML infrastructure, lead deployment of high-impact models, and drive MLOps best practices for AI-powered digital products.
Role type
Senior ML Engineering Lead (MLOps)
Builds
End-to-end ML pipelines, serving systems, monitoring, and governance layers for forecasting, optimization, and NLP models.
Domain
AI/ML Engineering, Cloud Infrastructure (Azure, Multi-cloud)
Deliverable
production ML models
Required skills
ML Engineering, MLOps, Spark, Azure Databricks, MLflow, Kubernetes, Docker, CI/CD, Model Registry, Model Monitoring, Containerization
Preferred skills
Feature Stores, Feature Engineering, AutoML, H2O, Leadership in ML/DevOps teams
Technologies
Azure Pipelines, MLflow, Databricks, Kubernetes, Docker, Spark
Responsibilities
Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance; Lead deployment of high-impact models; Design advanced CI/CD workflows; Implement model registry, versioning, lineage, and audit compliance; Build monitoring systems for model drift and retraining automation; Mentor MLOps engineers and guide cross-functional platform integration; Drive adoption of MLOps best practices.
Seniority
Senior, hands-on IC with mentorship responsibilities
