Job
Description
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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 tomorrowpeople 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.About The Role :
Job SummaryWe 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
Bachelors or Masters 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.
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