ML Ops Engineer
Core
Design, build, and improve machine learning solutions in a dynamic cloud environment to solve real business problems, moving from prototype to production.
Role type
Applied ML Engineer / Data Scientist
Builds
Production ML models and inference paths for marketing platforms
Domain
AI-Powered Marketing Cloud / Consumer Signals
Deliverable
production ML models
Required skills
Machine learning, statistics, experiment design, Python, cloud environment deployment (AWS), feature engineering, model evaluation, tradeoff analysis, LLM/GenAI workflows
Preferred skills
scikit-learn, PyTorch, TensorFlow, XGBoost, ML experiment tracking (MLflow, W&B), SQL, data warehouses/lakes, pipeline tools (Airflow, dbt, Spark), feature stores, vector search, HTTP/gRPC APIs, Docker, CI/CD (GitLab CI)
Technologies
AWS, Python, scikit-learn, PyTorch, TensorFlow, XGBoost, MLflow, W&B, SQL, Airflow, dbt, Spark, Docker, GitLab CI
Responsibilities
Explore data and develop models, run rigorous experiments, build inference paths, monitor performance, iterate after launch, own work from problem framing to rollout
Seniority
Mid-level to Senior (3+ years experience required)