Manager, AI Platform Engineering , Agent Platform Organization
Amazon (AI roles) · Toronto, Ontario, CAN
No salary listed. Estimated from 1122 salary-disclosed Engineering roles on this board: $201K–$307K (interquartile range; estimate, not the employer's figure).
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We are looking for a Manager of AI Platform Engineering to lead a team of AI platform engineers building and operating the data pipelines, models, and platform infrastructure that power core analytics, science and AI intatives. You will own the delivery and operational health of multiple business domains, build and mentor a high-performing team, and help evolve our self-service offerings from reactive dashboards to proactive agent driven approaches.
Your team will use AI development IDEs and generative AI tooling daily, and will build multi-agent solutions that automate common data engineering and BI tasks (e.g. pipeline generation, data quality enforcement, testing, and operational response). You will guide this evolution, helping your engineers develop fluency with agentic tooling while maintaining the data engineering fundamentals that everything depends on. You will also help your engineers develop and deploy agents, that solve common Analytics use cases around monitoring, anomaly detection, and insight generation.
You will also partner with business intelligence, applied science, and product teams to translate data needs into technical roadmaps, and contribute to shared platform infrastructure when the work calls for it.
- 3+ years of engineering team management experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience working with technical and product stakeholders to define requirements, prioritize features, and influence product roadmaps
- - 7+ years of working directly within data engineering or closely related teams, with hands-on contribution to data platform and pipeline delivery
- - 3+ years of designing or architecting data systems, including data modeling, pipeline patterns, reliability, and scaling strategies
- - 5+ years of experience building or leading development of data pipelines and cloud-native data infrastructure (e.g., data warehouses, data lakes, event-driven architectures, orchestration platforms)
Your team will use AI development IDEs and generative AI tooling daily, and will build multi-agent solutions that automate common data engineering and BI tasks (e.g. pipeline generation, data quality enforcement, testing, and operational response). You will guide this evolution, helping your engineers develop fluency with agentic tooling while maintaining the data engineering fundamentals that everything depends on. You will also help your engineers develop and deploy agents, that solve common Analytics use cases around monitoring, anomaly detection, and insight generation.
You will also partner with business intelligence, applied science, and product teams to translate data needs into technical roadmaps, and contribute to shared platform infrastructure when the work calls for it.
- 3+ years of engineering team management experience
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience working with technical and product stakeholders to define requirements, prioritize features, and influence product roadmaps
- - 7+ years of working directly within data engineering or closely related teams, with hands-on contribution to data platform and pipeline delivery
- - 3+ years of designing or architecting data systems, including data modeling, pipeline patterns, reliability, and scaling strategies
- - 5+ years of experience building or leading development of data pipelines and cloud-native data infrastructure (e.g., data warehouses, data lakes, event-driven architectures, orchestration platforms)
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