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.

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