Ml Ops Engineer Specialist (Ai Engineering)

TL;DR

ML Ops Engineer Specialist (AI Engineering): Design and implement robust infrastructure to enable scalable, reliable, and reproducible machine learning workflows with an accent on versioning, training, testing, and deployment of machine learning models across a variety of environments. Focus on continuous integration, model monitoring, auto-scaling, and failover.

Location: Must be based in Brazil

Salary: $30+ per hour

Company

hirify.global, by Invisible, is looking for a ML Ops Engineer Specialist.

What you will do

• Build scalable ML infrastructure to support versioning, training, testing, and deployment of machine learning models.
• Work closely with ML researchers, data scientists, and backend engineers to create 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, reducing latency and cost while maintaining high reliability.
• Document best practices in MLOps methodologies, including 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 consultants & contractors who run/operate their own business are welcome.
• As a contractor you’ll supply a secure computer and high‑speed internet.
• Company‑sponsored benefits such as health insurance and PTO do not apply.

Hiring process

• All candidates must pass an interview as part of the contracting process.
• The exact rate is determined after evaluating your experience, expertise, and geographic location.
• Final offer amounts may vary from the pay range listed above.