MLOps Engineer

Sequoia Connect

MLOps Engineer monitoring, deploying, and governing production AI models for a global IT services powerhouse. Supporting Responsible AI, CI/CD, observability, and incident response across international projects.

Posted 8/22/2026full-timeRemote • 🇲🇽 MexicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in monitoring and managing AI models in production, ensuring adherence to Responsible AI principles while collaborating effectively across product, engineering, and data teams. Proficient in Python, MLOps, and CI/CD practices, with a strong focus on performance, reliability, and transparency in AI systems.

Highest-signal resume keywords

Python ProgrammingMLOps ExpertiseCI/CD Pipeline ExperienceContainerization with DockerCloud Platform Familiarity (Azure)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingMLOpsMonitoring SystemsLogging SystemsCI/CD PipelinesContainerizationDockerAI Systems ExperienceProduction TroubleshootingModel Performance Monitoring

Soft Skills

ResilienceEmotional IntelligenceAgile Delivery FocusCross-Functional Collaboration

Tools & Technologies

Monitoring PlatformsLogging PlatformsAlerting Platforms

Industry Keywords

Responsible AIModel DriftData Distribution ChangesAudit TrailsFairness MonitoringBias MonitoringExplainability MonitoringTransparency Monitoring

Tech Stack

Tools & technologies

AzureCloudDockerPython

About the role

Key responsibilities & impact

Monitor AI models and agents in production for performance, latency, errors, availability, model drift, and data distribution changes
Detect and triage production incidents related to AI behavior
Execute rollbacks, throttling, or model disabling when thresholds are breached
Support deployment, versioning, and release of AI models and agents using CI/CD-style pipelines
Maintain registries covering model ownership and lineage
Ensure AI systems adhere to Responsible AI principles
Maintain audit trails and support fairness, bias, explainability, and transparency monitoring in production
Integrate AI systems with monitoring, logging, and alerting platforms
Collaborate with product, engineering, and data teams to standardize AI Ops patterns

Requirements

What you’ll need

Strong Python skills and experience supporting ML or LLM-based systems
Deep understanding of Model Ops / MLOps, especially the operational phase after deployment
Experience with monitoring and logging systems
Experience with CI/CD pipelines
Experience with containerized/containerised deployments, including Docker-based runtimes
Familiarity with cloud platforms, with Azure preferred
Production troubleshooting experience
Ability to work cross-functionally with product, data science, engineering, and risk teams
Resilience, emotional intelligence, and focus on agile delivery
Deep understanding of the difference between coding and engineering
Advanced oral English
Advanced Spanish
Experience with AI

Benefits

Comp & perks

Access to global career opportunities and exposure to high-impact projects within an international network
Flexible work arrangement / remote work
Opportunity to work for an award-winning, sustainable employer
Work on end-to-end digital transformation projects for global leaders