Senior AI Foundations Software Engineer

Amazon Inc.; Redmond (WA); On-site; USD 168,000 - 227,000; Full time

Benefits offered by this job

Health insurance and benefits

RSUs and stock-based compensation

401(k) matching

Paid time off and parental leave

Job summary

Amazon Leo is building a Sr. Software Development Engineer role to design and operate globally distributed systems for low-latency analytics and AI-powered operational intelligence. You will architect agentic systems that monitor health, diagnose issues, and optimize Leo’s satellite constellation and gateways.

You will work with AWS technologies, implement reinforcement learning agents, and develop data lakehouse architecture to support AI retrieval patterns and real-time insights for customers.

Qualifications

• 5+ years of non-internship professional software development experience.
• 5+ years of programming with at least one software programming language experience.
• 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience.
• Experience as a mentor, tech lead or leading an engineering team.
• 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience.
• Bachelor's degree in computer science or equivalent.

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, elevate 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

Skills

Software development

Architecture design

Leadership

Mentorship

Education

Bachelor's degree in computer science or equivalent

Tools

Git

Job description

Amazon Leo is building a Sr. Software Development Engineer role to design and operate globally distributed systems for low-latency analytics and AI-powered operational intelligence. You will architect agentic systems that monitor health, diagnose issues, and optimize Leo’s satellite constellation and gateways.

You will work with AWS technologies, implement reinforcement learning agents, and develop data lakehouse architecture to support AI retrieval patterns and real-time insights for customers.