Core 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.

Required Profile

• 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.

Preferred Differentiators

• 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.

Success Measures

• Reusable deployment patterns are documented, automated, adopted, observable, and auditable across key agentic AI workloads.
• Dev, UAT, and Production release paths become faster, more secure, more consistent, and easier to govern.
• Security, governance, reliability, monitoring, and responsible automation are embedded into delivery workflows from the start.

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-HYBRID #LI-JL1

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