Sr. AI Platform Engineer

RealPage, Inc.•Richardson, TX

2d•$105,800 - $180,200•Remote

About The Position

This Senior Engineer turns RealPage's responsible agentic factory strategy into repeatable infrastructure, automated delivery pipelines, production-grade operating practices, and clear engineering standards. The role blends hands-on AI platform delivery with governance, observability, secure deployment, and mentoring across technical teams.

Requirements

8 to 10 years of engineering experience, including 7+ years building and operating production systems on cloud platforms, plus hands-on AI/ML service deployment in production.
2-3 years using AI coding tools (Claude Code, Codex, Cursor) to automate platform buildout, deployment, testing, troubleshooting, and documentation.
Strong experience with GCP/AWS or hybrid cloud/datacenter deployments; Docker, Kubernetes, GitHub Actions with reusable workflows and self-hosted runners, Terraform, and Helm.
Working knowledge of authentication and authorization (OAuth 2.0, OIDC, SAML, JWT, RBAC, IAM), including workload identity, service-to-service auth, and securing API and tool access.
4+ years scripting and automation experience, preferably Python and JavaScript, with strong troubleshooting across Linux, containers, Kubernetes, networking, and production incidents.
Ability to architect secure, cost-efficient hosting for open-source LLMs, on-prem or in dedicated cloud, as an alternative to commercial model APIs.

Nice To Haves

Agentic AI frameworks (LangGraph, Google ADK), agent-to-agent (A2A) patterns, tool calling, AI workflow orchestration, RAG, evaluation, and responsible AI guardrail design.
AI-specific observability and tracing (LangSmith, Grafana/LGTM), GPU infrastructure for model serving (NVIDIA GPU Operator, Vertex AI), and DAG-based orchestration (Dagster, Prefect, Airflow).
Excellent communication, mentoring, stakeholder management, and project planning, with flexibility for critical deployments and production incidents.

Responsibilities

Define standards-based deployment patterns for responsible AI agents, reusable platform capabilities, and secure agent runtime architectures, including identity, access, and secrets management.
Automate AI platform buildout, release standardization, environment provisioning, CI/CD, Terraform/Helm deployments, and operational runbooks.
Operate reliable AI platform services with incident response, capacity planning, OpenTelemetry traces/logs/metrics, monitoring, rollback, and disaster recovery practices.
Collaborate across InfoSec, CloudOps, DevOps, and platform engineering teams to create responsible agentic factory standards for guardrails, governance, secure releases, observability, and production readiness.
Coach engineers, lead design reviews, guide implementation toward approved architecture patterns, and drive practical tradeoffs across cost, speed, reliability, and security.