MLOps Engineer / ML Platform Engineer

Turtle Trax S.A.; Mexico; On-site; MXN 600,000 - 1,000,000; Full time

Job summary

Turtle Trax S.A. is hiring an AI Engineer – AI Ops to monitor and maintain AI models in production, ensuring reliability, performance, and governance across deployments.

You will track model health, manage incidents, and support CI/CD-like workflows for model/version lifecycles, while aligning with risk, compliance, and product teams. The role emphasizes responsible AI practices, observability, and cross-functional collaboration in a fast-paced environment.

Qualifications

• Strong Python skills and experience supporting ML or LLM-based systems.
• Understanding of Model Ops / MLOps, especially the operational phase after deployment.
• Experience with monitoring and logging systems.
• Experience with CI/CD pipelines.
• Containerised deployments (e.g. Docker-based runtimes).
• Familiarity with cloud platforms (Azure preferred) and production troubleshooting.
• Ability to work cross-functionally with product, data science, engineering, and risk teams.

Responsibilities

• Monitor AI models and agents in production for performance, latency, errors, and availability.
• Track health indicators like drift and data distribution changes.
• Observe business KPIs linked to AI behaviour and efficiency.
• Detect and triage production incidents related to AI behaviour or degradation.
• Execute rollbacks, throttling, or model disabling where thresholds are breached.
• Support root‑cause analysis and post‑incident reviews to prevent recurrence.
• Support deployment, versioning, and release of AI models and agents using CI/CD-style pipelines.
• Maintain registries and metadata covering ownership, lineage, risk, and approvals.
• Move models/agents safely through dev → test → production environments.
• Ensure AI systems adhere to Responsible AI, controls, and audit requirements.
• Maintain audit trails, logs, and approval artefacts for risk/compliance regulators.
• Support fairness, bias, explainability, and transparency monitoring in production.
• Integrate AI systems with monitoring, logging, and alerting platforms; collaborate on cloud infra.

Skills

Python

Model Ops / MLOps

Monitoring & logging

CI/CD pipelines

Docker-based runtimes

Azure cloud

Cross-functional collaboration

Tools

Docker

Azure

Cloud platforms

Job description

AI Engineer – AI Ops (Monitoring AI Systems in Production)

Role purpose

An AI Engineer in AI Ops & Governance is responsible for operating, monitoring, and maintaining AI models and agent-based systems once they are live in production. The role ensures AI systems remain reliable, performant, compliant, and aligned with business expectations over time, closing the “last‑mile” gap between model development and sustainable production use

Key Responsibilities

Production Monitoring & Health

• Monitor AI models and agents in production for performance, latency, errors, and availability.
• Track statistical health indicators such as model drift, data distribution changes, and output stability.
• Observe business KPIs linked to AI behaviour (e.g. accuracy impact, false‑positive cost, efficiency).

Incident Management & Recovery

• Detect and triage production incidents related to AI behaviour or degradation.
• Execute rollbacks, throttling, or model disabling where thresholds are breached.
• Support root‑cause analysis and post‑incident reviews to prevent recurrence.

Model & Agent Lifecycle Operations

• Support deployment, versioning, and release of AI models and agents using CI/CD‑style pipelines.
• Maintain registries and metadata covering model ownership, lineage, risk classification, and approvals.
• Support models and agents move safely through environments (dev → test → production).

Governance, Risk & Compliance

• Ensure AI systems adhere to Responsible AI principles, internal controls, and audit requirements.
• Maintain audit trails, logs, and approval artefacts required by risk, compliance, and regulators.
• Support fairness, bias, explainability, and transparency monitoring in production.

Tooling & Platform Integration

• Integrate AI systems with monitoring, logging, and alerting platforms (e.g. dashboards, metrics stores).
• Work with cloud infrastructure (containers, event streaming, APIs) supporting scalable AI operations.
• Collaborate with product, engineering, and data teams to standardise AI Ops patterns and blueprints.

Skills & Experience (Baseline)

• Strong Python skills and experience supporting ML or LLM‑based systems.
• Understanding of Model Ops / MLOps, especially the operational phase after deployment.
• Experience with:
  • Monitoring and logging systems
  • CI/CD pipelines
  • Containerised deployments (e.g. Docker‑based runtimes)
  • Familiarity with cloud platforms (Azure preferred) and production troubleshooting.
  • Ability to work cross‑functionally with product, data science, engineering, and risk teams