AI Architect I
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Job Description
Agentic AI Architect designing scalable, reusable architectures for RealPage’s production AI agents. Translating workflows into reliable orchestration, integration, evaluation, and observability patterns.
Responsibilities:
- Design end-to-end architectures for agentic AI solutions, including orchestration, runtime execution, workflow management, tool invocation, system integrations, evaluation, observability, and supportability.
- Translate product requirements, business workflows, and customer use cases into technical designs and implementation plans.
- Define reusable patterns for multi-step agent behavior, task routing, handoffs, cross-system interactions, state management, and multi-agent coordination.
- Partner with AI Platform teams to use shared orchestration, runtime, evaluation, reporting, monitoring, and developer-tooling capabilities.
- Collaborate with engineering teams through architecture reviews, design guidance, technical documentation, and production-readiness assessments.
- Evaluate tradeoffs involving scalability, reliability, performance, security, maintainability, cost, developer experience, and delivery speed.
- Define integration patterns for APIs, enterprise systems, data sources, tools, model services, and Model Context Protocol capabilities.
- Establish observability requirements, including logging, metrics, traces, dashboards, alerts, debugging workflows, and operational reporting.
- Partner with QA/Eval teams on evaluation criteria, regression coverage, testing strategies, and quality controls.
- Support release planning, launch-readiness reviews, production monitoring, incident response, and post-release improvement.
- Identify repeated solution patterns and recommend shared product or platform capabilities.
- Contribute to architecture standards, reference designs, technical decision records, implementation guidance, and reusable documentation.
- Help teams apply responsible AI, privacy, security, governance, and human oversight requirements.
- Communicate technical decisions, dependencies, risks, and recommendations to engineering, product, operations, and business stakeholders.
Requirements:
- Experience designing or building backend systems, distributed applications, workflow platforms, integration services, developer platforms, cloud applications, or AI-enabled solutions.
- Working knowledge of APIs, asynchronous processing, service coordination, event-driven architectures, stateful systems, and enterprise integrations.
- Understanding of architecture principles related to scalability, reliability, performance, availability, maintainability, security, and production support.
- Familiarity with LLM-based applications, AI agents, retrieval techniques, prompt and context management, tool calling, workflow automation, or model-driven products.
- Ability to convert business and workflow requirements into practical technical architectures.
- Experience with cloud services, deployment pipelines, monitoring, logging, dashboards, and production troubleshooting.
- Understanding of testing, evaluation, regression coverage, release readiness, and operational support for production systems.
- Ability to evaluate technical alternatives and explain architecture tradeoffs to technical and nontechnical stakeholders.
- Strong written and verbal communication skills, including the ability to produce clear diagrams, design documents, decision records, and implementation guidance.
- Strong collaboration skills across engineering, product, QA/Eval, security, infrastructure, operations, and support teams.
- Ability to work effectively in an evolving technical environment.
- Preferred: experience designing or implementing production LLM applications, AI agents, workflow automation, or intelligent assistant solutions.
- Preferred: familiarity with agent orchestration frameworks, model gateways, evaluation frameworks, vector search, retrieval-augmented generation, and AI observability.
- Preferred: experience integrating AI solutions with enterprise platforms, internal services, APIs, data sources, and third-party tools.
- Preferred: familiarity with Model Context Protocol, tool integration primitives, structured outputs, agent memory, workflow state, and multi-agent patterns.
- Preferred: experience with responsible AI practices, including safety controls, access management, data protection, human oversight, and auditability.
- Preferred: experience creating reusable reference architectures, platform patterns, technical standards, or architecture governance.
- Preferred: familiarity with production incident management, reliability engineering, deployment strategies, and post-release support.
- Relevant cloud, software architecture, data, security, or AI certifications are beneficial but not required.
Benefits:
- Health, dental, and vision insurance.
- Retirement savings plan with company match.
- Paid time off and holidays.
- Professional development opportunities.
- Performance-based bonus based on position.
- Certain roles are eligible for additional rewards, including annual bonus and sales incentives depending on the applicable plan, role, and individual performance.
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