{"id":2029751,"url":"https://alion.io/job/metropolis-technologies-senior-software-engineer-product-foundations-4","title":"Senior Software Engineer, AI Agentic Platform","company":{"id":5152,"name":"Metropolis Technologies","domain":"metropolis.io","url":"https://alion.io/company/metropolis-technologies","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":93,"open_postings":62,"ghost_share":0,"stale_share":0.065,"repost_share":0.016,"time_to_fill_p50_days":62,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":177000,"max":216000,"currency":"USD","period":"year","gross":null,"usd_annual":216000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"AIOps","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"GitHub","optional":false},{"name":"Google ADK","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"Semantic Search","optional":false},{"name":"TypeScript","optional":false},{"name":"Datadog","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Git","optional":true},{"name":"LangChain","optional":true},{"name":"Machine Learning","optional":true},{"name":"MySQL","optional":true},{"name":"PostgreSQL","optional":true},{"name":"RLAIF","optional":true},{"name":"RLHF","optional":true},{"name":"Scala","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-07T13:42:03Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-26T16:41:12Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":18},"description":"Who we are\nThe real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy - a future where mundane repetition disappears and being known unlocks access, comfort, and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn't coming; it's here, and we need builders, innovators, and problem solvers to help us create it.\nWho you are\nMetropolis is seeking a Senior Software Engineer to architect and build our AI agent swarm, orchestration pipelines, and developer tooling. In this role, you will solve complex distributed systems challenges at scale, make critical build-vs-buy decisions on rapidly-evolving AI infrastructure, and construct low-latency agent orchestration frameworks that enable product engineering teams to deploy autonomous workflows seamlessly supporting both SDLC and business process automation. Working alongside highly technical hypernerds who live and breathe performance optimization, model routing, reliability engineering, and agent protocols, you will collaborate across Application Engineering, Infrastructure, and Advanced Technologies to deliver remarkable, checkout-free real-world experiences.\nWhat you'll do\nDesign and build production-grade agentic platform infrastructure, including harness, orchestration pipelines, tool-use abstractions, MCP integrations, and context/memory management systems.\nArchitect high-throughput, low-latency LLM inference routing, caching strategies, and semantic search interfaces to optimize cost, performance, and token usage\nEstablish robust evaluation, tracing, and observability frameworks (LLMOps/AIOps) to measure agent accuracy, monitor execution paths, and debug non-deterministic workflows\nBuild developer tooling, agent SDKs, and CI/CD automation that empower product engineering teams to deploy autonomous AI agents safely and efficiently\nImplement enterprise-grade guardrails, rate-limiting, sandboxing, and security policies for model execution and tool access\nMake critical architectural choices regarding service boundaries, data consistency, multi-agent communication protocols, and overall system reliability\nDrive technical standards, evaluate emerging AI tooling, and mentor engineering peers on agentic architectural patterns\nWhat we're looking for\n6+ years of backend software engineering experience with deep focus on distributed systems, platform infrastructure, and high-load production environments\n2+ years of hands-on experience architecting and deploying agentic AI systems, LLM orchestration frameworks, or AI platform tooling\nStrong proficiency in Python or Java/TypeScript with production expertise in async concurrency, API design, and system performance tuning\nProduction experience with modern agentic orchestration frameworks and protocols such as LangGraph, Anthropic MCP, Google ADK, AutoGen, A2A, or LlamaIndex\nExpertise in microservices architecture, container orchestration (Kubernetes), and cloud platforms (AWS)\nObservability and LLMOps expertise, including metrics, distributed tracing, structured logging, and debugging agent execution runs\nDatabase and data systems knowledge including relational databases, vector databases, event streaming (Kafka, SQS), and caching patterns\nDemonstrated experience leveraging AI-powered development tools such as Claude Code, GitHub Copilot, or similar agentic coding tools for enhanced productivity and context engineering\nExcellent technical communication with the ability to design and document complex AI architectures and collaborate across cross-functional team\nWhile not required, these are a plus:\nAdvanced degree in Computer Science, Machine Learning, or related technical field\nExperience building customized agent harnesses, fine-tuning models, or implementing RLHF/RLAIF optimization loops\nSecurity compliance expertise regarding data privacy, prompt injection defenses, secrets management, and access controls for financial or mobility data\nContributions to open-source agent frameworks, MCP servers, or technical writing on agentic system architectures\nOur Stack\nLanguages + Frameworks: TypeScript, React, Scala (principally), Java (limited)\nDatastores: MySQL, PostgreSQL, Snowflake\nCloud: AWS\nVersion control: Git & GitHub\nAI Tooling: Copilot on GitHub and Claude Code\nObservability: Datadog\n4 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\nWhen 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\nMetropolis 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.\nAs 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.\nMetropolis 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.\nWe are committed to providing a welcoming, accessible hiring experience. 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