MLE - HF

Company Overview

[Company overview not provided.]

Job Summary

The Machine Learning Engineer (MLE) - HF will be responsible for implementing and managing machine learning models, leveraging various tools and services to enhance predictive accuracy and operational efficiency. This role is essential in driving the organization's AI initiatives, ensuring high-quality model performance and seamless integration within existing systems.

Responsibilities

• Understand and apply core machine learning algorithms and evaluation metrics in practical scenarios.
• Conduct hands-on training and testing of machine learning models on a small scale.
• Utilize Python programming and related libraries (e.g., pandas, NumPy, matplotlib, scikit-learn) for AI and ML projects.
• Work with Google Cloud Platform (GCP) services, including Vertex AI, BigQuery, Cloud Storage, and Cloud Run.
• Implement MLOps practices by managing version control using GitHub and understanding CI/CD pipelines tailored for AI/ML applications.
• Develop Infrastructure as Code (IaC) solutions by writing Terraform templates to manage GCP components effectively and leveraging Docker for deployment.
• Gain experience in observability and evaluation, including GCP observability services and potential tools like Arize Observability.

Qualifications

• Technical Skills:
  • Proficient in Machine Learning principles and model evaluation techniques.
  • Strong knowledge of Python programming and relevant libraries.
  • Experience with GCP services, particularly in machine learning contexts.
  • Familiarity with MLOps and CI/CD pipelines for ML applications.
  • Competence in Infrastructure as Code (IaC) using Terraform and Docker.
• Educational Qualifications:
  • Degree in Computer Science, Data Science, Engineering, or a related field (BSc, MSc preferred).

Preferred Skills

• Experience with observability tools, ideally with GCP's prompt/response services or Arize Observability.
• Knowledge of advanced ML methodologies and practices.

Experience

• [Experience requirements not specified.]

Environment

This position is remote, allowing flexibility in work location while collaborating with team members across various regions.

Salary

[Salary details not provided.]

Growth Opportunities

[Details about career advancement opportunities not provided.]

Benefits

[Benefits information not provided.]