Principal SaaS Capacity Engineer

Oracle; Región Centro; Presencial; MXN 400,000 - 600,000; Jornada completa

Descripción de la vacante

A global technology company in Jalisco seeks a seasoned professional for capacity engineering, focusing on SaaS optimization and AI/ML model integration. Ideal candidates will have strong cloud infrastructure experience, relevant degrees, and proven skills in programming and automation tools. Join a collaborative team to enhance operational excellence and contribute to innovative capacity planning strategies.

Formación

• 5+ years of experience in cloud infrastructure or related roles.
• Hands-on experience with large-scale distributed systems.
• Deep understanding of cloud capacity topology.

Responsabilidades

• Ensure SaaS production capacity availability and optimization.
• Apply AI/ML models for capacity forecasting and anomaly detection.
• Collaborate with Product teams on AI-driven strategies.

Conocimientos

Cloud infrastructure experience

Programming and scripting (Python, Go, Shell, SQL)

Expertise in AI/ML models for capacity engineering

Infrastructure-as-code (Terraform, Ansible, Helm, Kubernetes)

AIOps tools knowledge (Datadog, Dynatrace, Splunk)

Educación

Bachelor’s or Master’s degree in Computer Science or related field

Descripción del empleo

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Cloud/Systems Engineering, or a related field.
• 5+ years of experience in cloud infrastructure, SaaS operations, or capacity engineering roles.
• Hands-on experience with large-scale distributed systems, OCI (or AWS, Azure, GCP), and SaaS production environments.
• Strong programming and scripting experience (Python, Go, Shell, SQL) for automation and AI/ML model deployment.
• Proven experience deploying AI/ML solutions for capacity forecasting, anomaly detection, and intelligent workload tuning.
• Deep understanding of cloud capacity topology and distributed service dependencies.
• Proficiency with infrastructure-as-code (Terraform, Ansible, Helm, Kubernetes).
• Familiarity with AIOps tools and AI-driven observability platforms (Datadog, Dynatrace, Splunk, or similar).
• Knowledge of resiliency and disaster recovery strategies, including AI-simulated failure modeling.

Preferred Qualifications

• Advanced degree (Master’s/PhD) with specialization in AI, ML, Data Science, or distributed systems engineering.
• Experience building and deploying self-healing, AI-driven automation at scale in a SaaS environment.
• Domain expertise in reinforcement learning applications for automated resource optimization.
• Direct exposure to Oracle Cloud Infrastructure (OCI) systems and tools.
• Experience with cloud-native AI/ML services, MLOps, and continuous model monitoring.

Competencies and Skills

• Expertise in designing, developing, and deploying AI/ML models for cloud infrastructure use cases (demand forecasting, anomaly detection, workload optimization).
• Advanced proficiency in automation, orchestration, and self-healing system architectures.
• Skilled in communicating technical concepts, AI-powered analytics, and strategic insights to engineering and executive audiences.
• Strong analytical and critical thinking skills, with a deep data-driven mindset.
• Curiosity and initiative to explore APIs, system profiles, and operational anomalies, translating technical findings into impactful business outcomes.
• Highly collaborative, adaptive, and passionate about operational excellence and continuous learning.
• Ability to influence cross-team priorities and drive best practices in AI-enhanced capacity engineering.

Career Level

IC4

Key Responsibilities

• Service Accountability: Ensure SaaS production capacity availability, optimization, scaling automation, reserve management, and quota governance.
• AI/ML Integration: Apply AI/ML models for predictive capacity forecasting, anomaly detection, and workload auto-tuning to anticipate demand spikes and prevent outages.
• Observability & AIOps: Leverage AI-powered observability and AIOps platforms for end-to-end system monitoring, intelligent alerting, and automated incident mitigation.
• Strategic Partnership: Collaborate with Product and Development teams to design, validate, and align AI-driven scaling and capacity planning strategies with new launches and initiatives.
• Automation & Orchestration: Design, implement, and optimize automation and orchestration pipelines, including self-healing systems, policy-driven provisioning, and disaster recovery simulations, using AI to enhance reliability and operational resilience.
• Data-Driven Decision Support: Deliver advanced instrumentation, AI-powered analytics, and actionable dashboards to inform executives, engineering teams, and stakeholders.
• Technical Leadership: Translate complex OCI stack and cloud platform resources (compute, storage, DB, networking) into business-ready, AI-enhanced capacity solutions and performance profiles.
• Simulation & Resiliency: Use AI/ML models to simulate, validate, and improve resiliency and disaster recovery scenarios for faster, more robust recovery response.
• Collaboration & Communication: Present AI-driven insights, risks, and recommendations to engineering teams, ICs, and executives to illuminate capacity trends and data-driven priorities.
• Continuous Innovation: Assess new AI/ML techniques, AIOps platforms, and automation tools for ongoing improvements in infrastructure reliability, scalability, and cost optimization.