Why Attentive needs you
- Demonstrate the ability to analyze, troubleshoot, coordinate, and resolve complex infrastructure issues
- Build, operate, and maintain a low-latency, high volume ML serving layer covering both online and batch inference use cases
- Orchestrate Kubernetes and ML training / inference infrastructure exposed as an ML platform
- Expose and manage environments, interfaces, and workflows to enable ML engineers to develop, build, and test ML models and services
- Manage and expand our feature store implementation that allows ML teams to self-service data labeling, feature engineering, and batch inferencing
- Close the latency gap on model inference to online, real-time model serving
- Develop automation workflows to improve team efficiency and ML stability
- Analyze and improve efficiency, scalability, and stability of various system resources
- Partner with other teams and business stakeholders to deliver business initiatives
- Help onboard new team members, provide mentorship and enable successful ramp up on your team's code bases
About you
- You have been working in the areas of MLOps / Platform Engineering / DevOps / Infrastructure for 5+ years, and have an understanding of gold standard practices and best in class tooling for ML
- Your passion is exposing platform capabilities through interfaces that enable high performance ML practices, rather than designing ML experiments (this team does not directly develop ML models)
- You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
- You have experience building infrastructure for an ML platform and managing CPU and GPU compute
- You have a background in software development and are passionate about bringing that experience to bear on the world of ML infrastructure
- You have experience with Infrastructure as Code using Terraform and can’t imagine a world without it
- You understand the importance of CI/CD in building high-performing teams and have worked with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCD
- You are passionate about observability and worked with tools such as Splunk, Nagios, Sensu, Datadog, New Relic
- You are very familiar with containers and container orchestration and have direct experience with vanilla Docker as well as Kubernetes as both a user and as an administrator
Our environment
- We have access to Python, Snowflake, SQL, DBT
- Our data visualization tool is Looker
- Our product backend is Java and Python microservices coupled with Spark, Kinesis, Airflow, Snowflake, and Postgres, all hosted on AWS
- Our team supports stakeholders from Client Strategy, Product Management, Sales, Marketing, Finance, Engineering, Designers and the Leadership Team
- We believe our company will win in the long run through product innovation and data-driven decision-making
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