Senior Software Engineer, AI Agentic Platform

Metropolis•Seattle, WA
•$177,000 - $216,000•Onsite

About The Position

Metropolis is seeking a Senior Software Engineer to architect and build its AI agent platform, orchestration pipelines, and developer tooling. The role addresses distributed-systems challenges at scale and makes build-versus-buy decisions about evolving AI infrastructure. It involves creating low-latency agent orchestration frameworks that help product engineering teams deploy autonomous workflows for software development lifecycle (SDLC) and business process automation. The engineer will collaborate with Application Engineering, Infrastructure, and Advanced Technologies to support Metropolis’s checkout-free real-world experiences. The position follows an office-first model requiring corporate team members to work onsite at least four days a week. The anticipated annual base salary is $177,000 to $216,000; actual compensation depends on qualifications, experience, skills, education, certifications, and location. The total compensation package may also include healthcare benefits, a 401(k), disability coverage, basic life insurance, stock options, and bonus plans.

Requirements

  • At least 6 years of backend software engineering experience, with a deep focus on distributed systems, platform infrastructure, and high-load production environments.
  • At least 2 years of hands-on experience architecting and deploying agentic AI systems, LLM orchestration frameworks, or AI platform tooling.
  • Strong proficiency in Python or Java/TypeScript, with production expertise in asynchronous concurrency, API design, and system performance tuning.
  • Production experience with modern agentic orchestration frameworks and protocols, such as LangGraph, Anthropic MCP, Google ADK, AutoGen, A2A, or LlamaIndex.
  • Expertise in microservices architecture, Kubernetes container orchestration, and AWS cloud platforms.
  • Observability and LLMOps expertise, including metrics, distributed tracing, structured logging, and debugging agent execution runs.
  • Knowledge of relational databases, vector databases, event streaming such as Kafka or SQS, and caching patterns.
  • Demonstrated experience using AI-powered development tools such as Claude Code, GitHub Copilot, or similar agentic coding tools for productivity and context engineering.
  • Excellent technical communication skills, including the ability to design and document complex AI architectures and collaborate across functions.

Nice To Haves

  • Advanced degree in Computer Science, Machine Learning, or a related technical field.
  • Experience building customized agent harnesses, fine-tuning models, or implementing RLHF/RLAIF optimization loops.
  • Security compliance expertise regarding data privacy, prompt injection defenses, secrets management, and access controls for financial or mobility data.
  • Contributions to open-source agent frameworks or MCP servers, or technical writing on agentic system architectures.

Responsibilities

  • Design and build production-grade agent platform infrastructure, including harnesses, orchestration pipelines, tool-use abstractions, MCP integrations, and context and memory management systems.
  • Architect high-throughput, low-latency LLM inference routing, caching strategies, and semantic search interfaces to optimize cost, performance, and token usage.
  • Establish evaluation, tracing, and observability frameworks (LLMOps/AIOps) to measure agent accuracy, monitor execution paths, and debug non-deterministic workflows.
  • Build developer tooling, agent SDKs, and CI/CD automation to help product engineering teams deploy autonomous AI agents safely and efficiently.
  • Implement enterprise-grade guardrails, rate limiting, sandboxing, and security policies for model execution and tool access.
  • Make architectural decisions about service boundaries, data consistency, multi-agent communication protocols, and system reliability.
  • Drive technical standards, evaluate emerging AI tooling, and mentor engineering peers on agentic architectural patterns.
  • Collaborate across Application Engineering, Infrastructure, and Advanced Technologies.

Benefits

  • Healthcare benefits
  • 401(k) plan
  • Short-term and long-term disability coverage
  • Basic life insurance
  • Stock option plan
  • Bonus plans
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