ML Ops Engineer Specialist

hace 2 semanas

Argentina Invisible Agency A tiempo completo

Target Profile:

  • 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.

Technical Expertise:

  • 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).
  • Comfortable managing infrastructure via cloud services (GCP, AWS) and container orchestration platforms (e.g., Kubernetes).
  • Strong debugging and performance tuning skills across data, model, and infrastructure layers.

Bonus (Nice to Haves):

  • 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).

Project Overview & Deliverables:

Project Overview

  • You'll design and implement robust infrastructure to enable scalable, reliable, and reproducible machine learning workflows. You'll streamline the lifecycle of ML models, from experimentation to deployment, ensuring our systems are production-grade and future-proof.

Deliverables: 

  • Build Scalable ML Infrastructure: Architect, deploy, and maintain pipelines and tooling that support versioning, training, testing, and deployment of machine learning models across a variety of environments.
  • Bridge Research and Production: Work closely with ML researchers, data scientists, and backend engineers into efficient, production-ready services and APIs.
  • Focus on Automation and Reliability: Implement systems for continuous integration, model monitoring, auto-scaling, and failover, with a strong emphasis on observability and operational excellence.
  • Optimize Cloud Resources: 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: Document best practices in MLOps methodologies such as model versioning, reproducibility, metadata tracking, and experiment lineage..

Important:

All candidates must pass an interview as part of the contracting process.

We offer a pay range of $30+ per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. 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.

We are looking for independent consultants & contractors who run/operate their own business

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