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About This Role
Overview:
We are looking for an Agentic AI Architect I to help design scalable, reliable, and reusable architectures for production-grade AI agents. This role supports the AI Center of Excellence mission by helping product and engineering teams build, integrate, release, evaluate, and operate agentic AI solutions using shared platform capabilities and consistent architectural patterns.
You will work across product, engineering, AI Platform, infrastructure, QA/Eval, operations, and support teams to translate business and workflow requirements into practical agent architectures. Your work may span agent orchestration, runtime services, tool and system integrations, workflow execution, observability, evaluation, responsible AI controls, and production support.
This is a hands-on architecture role for someone who can balance technical design, implementation practicality, reliability, and delivery speed. The successful candidate will help teams avoid fragile one-off solutions by identifying reusable patterns that can be adopted across AI agent products.
Responsibilities: Primary 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 clear 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 where appropriate.
- Partner with AI Platform teams to use shared orchestration, runtime, evaluation, reporting, monitoring, and developer-tooling capabilities.
- Collaborate with engineering teams during implementation through architecture reviews, design guidance, technical documentation, and production-readiness assessments.
- Evaluate architecture tradeoffs involving scalability, reliability, performance, security, maintainability, cost, developer experience, and delivery speed.
- Help define integration patterns for APIs, enterprise systems, data sources, tools, model services, and Model Context Protocol, or MCP, capabilities.
- Establish observability requirements, including logging, metrics, traces, dashboards, alerts, debugging workflows, and operational reporting.
- Partner with QA/Eval teams to incorporate evaluation criteria, regression coverage, testing strategies, and quality controls into agent architecture.
- Support release planning, launch-readiness reviews, production monitoring, incident response, and post-release improvement.
- Identify repeated solution patterns and recommend when they should become 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 throughout the solution lifecycle.
- Communicate technical decisions, dependencies, risks, and recommendations clearly to engineering, product, operations, and business stakeholders.
Qualifications: Required Knowledge, Skills, and Abilities
- 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 where requirements and agentic AI patterns may continue to mature.
Preferred Qualifications
- Experience designing or implementing production LLM applications, AI agents, workflow automation, or intelligent assistant solutions.
- Familiarity with agent orchestration frameworks, model gateways, evaluation frameworks, vector search, retrieval-augmented generation, and AI observability.
- Experience integrating AI solutions with enterprise platforms, internal services, APIs, data sources, and third-party tools.
- Familiarity with Model Context Protocol, tool integration primitives, structured outputs, agent memory, workflow state, and multi-agent patterns.
- Experience with responsible AI practices, including safety controls, access management, data protection, human oversight, and auditability.
- Experience creating reusable reference architectures, platform patterns, technical standards, or architecture governance.
- 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.
SALARY AND BENEFITS
RealPage provides a competitive salary package along with a comprehensive benefit plan that includes:* 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.
#LI-JK1 #LI-REMOTE
Compensation may vary depending on your location, qualifications including job-related education, training, experience, licensure, and certification, that could result at a level outside of these ranges. Certain roles are eligible for additional rewards, including annual bonus, and sales incentives depending on the terms of the applicable plan and role as well as individual performance.
Equal Opportunity Employer: RealPage Company is an equal opportunity employer and committed to creating an inclusive environment for all employees. Pay Range: USD $177,500.00 - USD $322,500.00 /Yr.
Salary Context
This $177K-$322K range is above the 75th percentile for AI Architect roles in our dataset (median: $182K across 42 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 10,827 AI roles we're tracking, AI Architect positions make up 1% of the market. At RealPage Inc, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,640 based on 245 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($250K) sits 11% above the category median. Disclosed range: $177K to $322K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
RealPage Inc AI Hiring
RealPage Inc has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Richardson, TX, US. Compensation range: $161K - $322K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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