Our client, a property management technology company, is actively looking for a Senior AI Platform Engineer to join their team in Richardson, TX! This role is onsite hybrid a few times a month so local candidates are required.
***This is a permanent position***
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 Skills & Experience
- 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 Skills & Experience
- 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.