Senior MLOps Engineer II
New
L
Life360Consumer Technology
Within the US and CanadaFull-TimeSenior
SalaryFor candidates based in the US, the salary range for this position is $148,000 to $216,000 USD. For candidates based out of Canada, the salary range for this position is $171,500 to 201,000 CAD.
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Job Details
- Experience
- 5+ years of professional software engineering, DevOps, or data engineering experience, with at least 2 years dedicated to building and maintaining MLOps infrastructure.
- Required Skills
- DockerPythonSQLKubernetesMLFlowAirflowFastAPISparkCI/CDMLOps
Requirements
- 5+ years of professional software engineering, DevOps, or data engineering experience.
- At least 2 years of dedicated experience building and maintaining MLOps infrastructure.
- Strong proficiency in Python, including unit testing, modular design, and Git.
- Hands-on experience with Docker and Kubernetes (EKS, GKE, or native clusters).
- Familiarity with ML lifecycle and data tools such as MLflow, Kubeflow, SparkML, SQL, Spark/PySpark, dbt, and Airflow.
- Practical experience operating within major cloud ecosystems like AWS, GCP, or Databricks.
- Experience with FastAPI.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a related quantitative field.
- Ability to collaborate with cross-functional teams and drive technical outcomes.
- Strong communication and project leadership skills.
Responsibilities
- Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery.
- Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows.
- Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift.
- Provision and optimize cloud-based ML infrastructure using Infrastructure as Code (IaC) paradigms.
- Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development and efficient ML system maintenance.
- Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security.
- Improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML, including streaming tools like Kafka and Flink.
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