Server Jobs

About The Position

Requirements

• 4+ years of professional experience in software engineering, with demonstrated ownership of production systems.
• Strong Python skills and comfort with async patterns, API design, and cloud-native development on AWS.
• Hands-on experience building LLM-powered applications including agents, tool use, RAG, prompt engineering, and evaluation. You've shipped something real with LLMs, not just experimented.
• Systems thinking. You can design an agent platform that's modular, observable, and maintainable, not just a collection of scripts.
• Comfort working across the stack. You'll interface with cloud infrastructure, data systems, frontend teams, and end users and you're energized by that breadth.
• Strong written communication. Agents are only as good as their grounding context. You'll help shape how we encode domain knowledge for AI consumption.

Nice To Haves

• Experience with agent frameworks such as AWS Strands, LangGraph, CrewAI, Claude Agent SDK, or similar orchestration tools.
• Familiarity with MCP (Model Context Protocol) or similar tool-use / function-calling patterns for LLMs.
• Experience building developer platforms or internal tooling that other engineers use daily.
• Experience with observability and evaluation frameworks for AI/ML systems (tracing, cost analysis, quality metrics).
• Experience in an early-stage startup environment designing, building, and launching new products or major features from scratch.

Responsibilities

• Build the agentic platform that enables Bedrock engineers to create, deploy, and monitor AI agents on AWS. This includes agent orchestration, tool/MCP server integration, evaluation and observability, and production deployment pipelines.
• Ship the first agents grounded in Bedrock specificities. These include a Q&A agent for understanding system behaviors, a Field Triage agent for mapping real-world issues to spec nodes, and an Annotator agent that improves data labeling quality.
• Build workflow automation agents for internal productivity—Slack thread summarization, Linear ticket management, cross-tool orchestration—that make every team at Bedrock faster.
• Design and implement MCP servers that expose Bedrock's internal systems as tool interfaces for agents.
• Build evaluation and observability infrastructure for agents—tracing, cost tracking, quality metrics, and automated regression testing to ensure agents are reliable and improving.
• Iterate on agent UX patterns beyond simple chat—building interfaces that surface agent outputs in the right context (Slack, dashboards, CLI, pull requests) and enable human-in-the-loop workflows.
• Drive 100,000 lb excavators.