Amazon Leo is Amazon’s low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.

This role is for a Sr. Software Development Engineer who will design, implement, and operate globally distributed systems that enable Leo to achieve low single-digit-second query responses within a near real-time analytics layer or lakehouse, with a primary focus on agentic AI capabilities for autonomous operational intelligence and system optimization. You'll architect intelligent agent systems that continuously monitor, diagnose, and optimize the health and performance of Leo's satellite constellation, ground gateways, and customer terminals.

You'll build these systems using the latest AWS technologies and best-in-industry software engineering practices, creating the foundation for AI-powered operational intelligence across the Leo network.

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.


Key job responsibilities
* Architect multi-agent systems that continuously analyze telemetry data from satellites, ground gateways, and customer terminals to detect anomalies, predict failures, and autonomously recommend corrective actions
* Develop intelligent agents capable of reasoning across complex distributed systems to identify root causes of operational issues with minimal human intervention
* Build agentic workflows that autonomously triage system anomalies, escalate critical issues based on severity and impact, and generate actionable insights for operations teams
* Design agent-based frameworks that coordinate workflows across the constellation, enabling collaborative problem-solving between autonomous agents, ground systems, and operations teams
* Implement reinforcement learning-based agents that optimize system performance parameters in real-time based on environmental conditions, network demand, and operational constraints
* Develop natural language interfaces allowing operations teams to query system health status, request analyses, and receive AI-generated recommendations through conversational interactions
* Build RAG systems that combine real-time telemetry with historical operational data, technical documentation, and knowledge bases to provide context-aware insights
* Design hybrid search strategies combining dense vectors with sparse representations for optimal semantic retrieval across operational patterns and system behaviors
* Design and implement evaluation frameworks to measure agent performance, accuracy, and reliability across diverse operational scenarios
* Build automated testing pipelines for agent behavior validation, including unit tests, integration tests, and end-to-end scenario testing
* Establish metrics and monitoring systems to track agent decision quality, response times, and operational impact
* Create feedback loops that continuously improve agent performance through reinforcement learning and human-in-the-loop validation
* Architect and implement a scalable, cost-optimized S3-based Data Lakehouse that unifies structured and unstructured data from disparate sources across the Leo constellation
* Establish metadata management with automated data classification and lineage tracking to support both analytical queries and AI retrieval patterns
* Design and enforce standardized data ingestion patterns with built-in quality controls and validation gates for satellite telemetry, ground station metrics, and customer terminal data
* Architect and implement a scalable, cost-performance-optimized OLAP-based analytics layer capable of achieving low single-digit-second query responses for near real-time analytics
* Lead the design of semantic data models that balance analytical performance with AI retrieval requirements
* Implement cross-domain federated query capabilities with sophisticated query optimization techniques
* Architect a centralized metrics repository that becomes the source of truth for all Leo operational metrics
* Design extensible metrics schemas that support complex analytical queries while optimizing for AI retrieval patterns
* Implement robust data quality frameworks with staging-first policies and automated validation pipelines
* Develop intelligent orchestration for metrics generation workflows with comprehensive audit trails
* Architect a globally distributed vector database infrastructure capable of managing billions of embeddings with consistent sub-100ms retrieval times
* Design and implement hybrid search strategies for optimal semantic retrieval across operational documentation, telemetry patterns, and system knowledge bases
* Establish automated compliance validation frameworks ensuring data handling meets Amazon's security standards and export control requirements


A day in the life
This role is for a Sr. Software Development Engineer who will build new cloud services and APIs that facilitates and orchestrates simulation of software on Leo devices such as satellites, ground gateways, and customer terminals. You will be building low-latency, highly scalable architecture that are critical to getting high quality internet service to customers.

About the team
This role is for a Sr. Software Development Engineer who will build new cloud services and APIs that facilitate and orchestrate the Leo AI Foundations—enabling intelligent software operation across Leo devices such as satellites, ground gateways, and customer terminals. You will design and deliver low-latency, highly scalable architectures that are critical to providing high-quality internet service and AI capabilities to customers.