Ml Ops Engineer Specialist (AI)

TL;DR

ML Ops Engineer Specialist (AI): Design and implement robust infrastructure to enable scalable, reliable, and reproducible machine learning workflows. Focus on automation and reliability, by implementing systems for continuous integration, model monitoring, auto-scaling, and failover, with a strong emphasis on observability and operational excellence.

Location: Argentina

Salary: $30+ per hour (approximately $4800+ monthly)

Company

Meridial Marketplace, by hirify.global, focuses on building and implementing machine learning workflows.

What you will do

• Architect, deploy, and maintain pipelines and tooling that support versioning, training, testing, and deployment of machine learning models across a variety of environments.
• Work closely with ML researchers, data scientists, and backend engineers into efficient, production-ready services and APIs.
• Implement systems for continuous integration, model monitoring, auto-scaling, and failover, with a strong emphasis on observability and operational excellence.
• Optimize compute resources across cloud and hybrid environments (e.g., GCP, AWS, on-prem), reducing latency and cost while maintaining high reliability.
• Document best practices in MLOps methodologies such as model versioning, reproducibility, metadata tracking, and experiment lineage.

Requirements

• 2+ years of experience building and maintaining ML infrastructure or platforms in production environments.
• Demonstrated ability to take ML models from experimentation to deployment using MLOps best practices.
• Experience collaborating with data scientists, ML engineers, and backend teams on cross-functional projects.
• Proficiency in Python and core ML tooling (e.g., MLflow, Kubeflow, Airflow, Docker, Git).
• Familiarity with model training frameworks such as PyTorch, ONNX, or scikit-learn.
• Experience with CI/CD pipelines tailored to ML systems (e.g., model validation checks, artifact versioning).

Nice to have

• Hands-on experience with Databricks or similar distributed compute environments.
• Familiarity with data engineering tools and workflow orchestration (Spark, dbt, Prefect).
• Knowledge of monitoring and observability stacks (Prometheus, Grafana, OpenTelemetry) for ML systems.
• Exposure to regulatory/compliance-aware ML deployment (audit logs, reproducibility, rollback strategies).

Culture & Benefits

• Independent consultant & contractor role.
• Secure computer and high‑speed internet required.
• Health insurance and PTO do not apply.