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.

Ml Ops Engineer Specialist (AI)