**Before you apply to a job, select your language preference from the options available at the top right of this page.** Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level. **Job Description:** **Job Summary** We are seeking a Senior MLOps / AIOps Platform Engineer with deep DevSecOps expertise and hands-on experience managing enterprise-grade AI/ML platforms. This critical role focuses on building, configuring, and operationalizing secure, scalable, and reusable infrastructure and pipelines that support AI and ML initiatives across the enterprise. The ideal candidate will have a strong background in Infrastructure as Code (IaC), pipeline automation, and platform engineering, with specific experience configuring and maintaining IBM watsonx and Google Cloud Vertex AI environments. **Key Responsibilities** Platform Engineering & Operations + Lead the provisioning, configuration, and ongoing support of **IBM watsonx** and **Google Cloud Vertex AI** platforms. + Ensure platforms are **production-ready, secure, cost-efficient, and performant** across training, inference, and orchestration workflows. + Manage lifecycle tasks such as patching, upgrades, integrations, and service reliability. + Partner with security, compliance, and product teams to align platforms with enterprise and regulatory standards. Enterprise MLOps / AIOps Enablement + Define and implement **standardized MLOps/AIOps practices** across business units for consistency and scalability. + Build and maintain **reusable workflows** for model development, deployment, retraining, and monitoring. + Provide **onboarding, enablement, and support** to AI/ML teams adopting enterprise platforms and tools. + Support **development/deployment of GenAI** applications and maintain them at an Enterprise scale. DevSecOps Integration + Embed **security and compliance guardrails** across the ML lifecycle, including CI/CD pipelines and IaC templates. + Implement **policy-as-code, access controls, vulnerability scanning, and automated compliance checks** . + Ensure all deployments meet **enterprise and regulatory requirements** (HIPAA, SOX, FedRAMP, etc.). Infrastructure as Code & Automation + Design and maintain **IaC templates** (Terraform, Pulumi, Ansible, CloudFormation) for reproducible ML infrastructure. + Build and optimize **CI/CD pipelines** for AI/ML assets including data pipelines, training workflows, deployment artifacts, and monitoring systems. + Enforce best practices around **automation, reusability, and observability** of infrastructure and workflows. Monitoring, Logging & Observability + Implement **comprehensive observability** for AI/ML workloads using Prometheus, Grafana, Stackdriver, or Datadog. + Monitor both **infrastructure health** (CPU, memory, cost) and **ML-specific metrics** (model drift, data integrity, anomaly detection). + Define KPIs and usage metrics to measure **platform performance, adoption, and operational health** . **Qualifications** Education + Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field. Experience + 5+ years in **MLOps, DevOps, Platform Engineering, or Infrastructure Engineering** . + 2+ years applying **DevSecOps practices** (secure CI/CD, vulnerability management, policy enforcement). + Hands-on experience configuring and managing **enterprise AI/ML platforms (IBM watsonx, Google Vertex AI)** . + Demonstrated success in building and scaling **ML infrastructure, automation pipelines, and platform support models** . Technical Skills + Proficiency with **IaC tools** (Terraform, Pulumi, Ansible, CloudFormation). + Strong scripting skills in **Python and Bash** . + Deep understanding of **containerization and orchestration** (Docker, Kubernetes). + Experience with **model lifecycle tools** (MLflow, TFX, Vertex Pipelines, or equivalents). + Familiarity with **secrets management, policy-as-code, access control** , and monitoring tools. + Working knowledge of **data engineering concepts** and their integration into ML pipelines. Preferred + **Cloud certifications** (e.g., GCP Professional ML Engineer, AWS DevOps Engineer, IBM Cloud AI Engineer). + Experience supporting platforms in **regulated industries** (HIPAA, FedRAMP, SOX, PCI-DSS). + Contributions to **open-source projects** in MLOps, automation, or DevSecOps. + Familiarity with **responsible AI practices** including governance, fairness, interpretability, and explainability. + Hands-on experience with **enterprise feature stores, model monitoring frameworks, and fairness toolkits** . **Employee Type:** Permanent UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.