What you'll do
Design and build production-grade agentic platform infrastructure, including harness, orchestration pipelines, tool-use abstractions, MCP integrations, and context/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 robust 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 that empower product engineering teams to deploy autonomous AI agents safely and efficiently
Implement enterprise-grade guardrails, rate-limiting, sandboxing, and security policies for model execution and tool access
Make critical architectural choices regarding service boundaries, data consistency, multi-agent communication protocols, and overall system reliability
Drive technical standards, evaluate emerging AI tooling, and mentor engineering peers on agentic architectural patterns
What we're looking for
6+ years of backend software engineering experience with deep focus on distributed systems, platform infrastructure, and high-load production environments
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 async 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, container orchestration (Kubernetes), and cloud platforms (AWS)
Observability and LLMOps expertise, including metrics, distributed tracing, structured logging, and debugging agent execution runs
Database and data systems knowledge including relational databases, vector databases, event streaming (Kafka, SQS), and caching patterns
Demonstrated experience leveraging AI-powered development tools such as Claude Code, GitHub Copilot, or similar agentic coding tools for enhanced productivity and context engineering
Excellent technical communication with the ability to design and document complex AI architectures and collaborate across cross-functional team
While not required, these are a plus
Advanced degree in Computer Science, Machine Learning, or 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, MCP servers, or technical writing on agentic system architectures
Our Stack
Languages + Frameworks: TypeScript, React, Scala (principally), Java (limited)
Datastores: MySQL, PostgreSQL, Snowflake
Cloud: AWS
Version control: Git & GitHub
AI Tooling: Copilot on GitHub and Claude Code
Observability: Datadog
4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection
When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $177,000.00 USD to $216,000.00 USD annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base salary is one component of Metropolis' total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans, and more. #LI-LR1 #LI-Onsite
Metropolis may utilize an automated employment decision tool (AEDT) to assess or evaluate your candidacy for employment or promotion. AEDTs are used to assist in assessing a candidate’s application relative to the required job qualifications and responsibilities listed in the job posting.
As part of this process, Metropolis retains data relevant to your candidacy, including personal information, for a period that is reasonably necessary for the use of the tool. If you are hired for the position, your data may become part of your employee records.
Metropolis Technologies is an equal opportunity employer. We make all hiring decisions based on merit, qualifications, and business needs — without regard to race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, disability, veteran status, or any other protected characteristic under federal, state, or local law.
We are committed to providing a welcoming, accessible hiring experience. If you need a reasonable accommodation for any part of the application or interview process, please reach out to us at recruiting@metropolis.io.
