Senior AI Platform Engineer

Jobtailor

Richardson (TX)

On-site

USD 150,000 - 210,000

Full time

14 days+

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Job summary

Jobtailor is seeking an experienced Cloud Platform Engineer to own deployment patterns for responsible AI agents and scalable platform capabilities. You will automate buildouts, releases, and environment provisioning while maintaining secure, observable AI services.

You will partner with InfoSec, CloudOps, and DevOps to ensure governance, guardrails, and robust production readiness across our cloud environments (GCP/AWS).

Qualifications

8 to 10 years of engineering experience with 7+ years on production cloud systems.
Hands-on AI/ML service deployment in production environments.
Strong cloud and automation tooling experience (GCP/AWS, Terraform, Helm).

Responsibilities

Define standards-based deployment patterns for responsible AI agents and secure runtimes.
Automate platform buildout, release standardization, and environment provisioning.
Operate reliable AI platform services with incident response and observability.
Collaborate across teams to ensure governance, guardrails, and production readiness.

Skills

AI/ML Service Deployment

Cloud Platform Engineering

Terraform and Helm

Authentication and Authorization

Scripting and Automation

Tools

GCP

AWS

Docker

Kubernetes

GitHub Actions

OpenTelemetry

CI/CD

Linux

RBAC

IAM

Job description

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.

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.

Core Competencies

Demonstrates expertise in defining deployment patterns for responsible AI agents and automating AI platform buildout, with a strong focus on secure architectures and incident response. Proven ability to collaborate across teams and coach engineers in implementing best practices for production readiness.

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