Ml Ops Engineer Specialist

hace 4 días

Buenos Aires, Argentina Invisible Agency A tiempo completo

**What You'll Do**

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.
- **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 to translate prototypes 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**: Manage and 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 and deliver best practices in MLOps methodologies such as model versioning, reproducibility, metadata tracking, and experiment lineage..

**What We Need**

**Professional Experience**:

- 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).

We offer a pay range of $35-to
- $50 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.

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

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