Senior Machine Learning Operations Engineer II (AI Native)
Core
Design, build, and scale infrastructure and automated pipelines to reliably train, deploy, and monitor machine learning models in production environments for AI-driven consumer products.
Role type
Senior MLOps Engineer
Builds
Production ML models, automated CI/CD and Continuous Training pipelines, high-availability microservices, and scalable cloud infrastructure.
Domain
Consumer technology, AI-native development, location-based services
Deliverable
production ML models
Required skills
Python, Kubernetes, Docker, MLflow, Airflow, Terraform, SQL, PySpark, Kafka, Flink, FastAPI, cloud networking, security, IaC
Preferred skills
Feature stores (Feast, Tecton), LLM serving frameworks (vLLM, Triton, TGI), distributed data engines (Ray, Dask), subscription product experience, geospatial data, mobile location services
Technologies
Kubernetes, Docker, MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark, PySpark, dbt, Airflow, AWS, GCP, Databricks, Terraform, Kafka, Flink, FastAPI, vLLM, Triton, TGI, Ray, Dask, Feast, Tecton
Responsibilities
Design and manage automated CI/CD and Continuous Training pipelines; Containerize and deploy ML models as microservices; Establish logging, alerting, and monitoring for model performance and drift; Provision and optimize cloud-based ML infrastructure; Collaborate with product teams on SDK/API development; Implement lineage tracking for data, code, and model artifacts.
Seniority
Senior, hands-on IC