Key skills
PythonAIMachine LearningMLAgenticMLOpsMLflowKubeflowSparkGlueLeadership
About this role
Attentive is an AI marketing platform redefining brand and customer connections through personalized experiences. They are seeking a Staff Software Engineer to lead the development of the Machine Learning Platform, focusing on building AI and ML infrastructure to enhance their product suite and improve operational efficiency.
Responsibilities:
- Setting Technical Direction - Architect ML platform strategy spanning data pipelines, training infrastructure, and serving layers using cutting-edge tooling like Ray, MLFlow, Metaflow, Argo, and Spark
- Uplevel and Innovate Core AI & ML Stack - Build and operate production-grade, low-latency ML serving layers with robust model lifecycle systems including champion/challenger testing, automated rollouts, versioning, and rollback capabilities
- Uplevel and Innovate Core AI & ML Stack - Define and drive Attentive’s agentic stack
- Technical Leadership - Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams
- Technical Mentorship - Mentor platform and ML engineers, actively championing team members
- Being the “Glue” - Build universal interfaces, architectures, and patterns—like data access layers and prediction serving APIs—that bridge platform capabilities with product needs to streamline high-priority ML work across the organization
Requirements:
- 5+ years focused specifically on ML Platform/MLOps, with deep understanding of gold-standard practices and best-in-class tooling
- Proven track record of owning and building core components of ML platforms using tools like Spark, Ray, MLFlow, Kubeflow, or Metaflow
- You've built and operated a high-throughput agentic stack (MCP / data infrastructure, context store, orchestration, and prompt layer)
- Strong expertise in Python for both batch processing and online service frameworks
- Experience designing and operating online and offline inference systems, understanding the critical differences and tradeoffs between them