MLOps Engineer

Our client, a global leader in mobile app monetization, is looking for an experienced MLOps Engineer. This role involves managing AWS-based machine learning infrastructure and building MLOps pipelines to support the company's large-scale machine learning projects. 

Key Responsibilities

• Design and deploy ML infrastructure using AWS services.
• Build and maintain ML-oriented CI/CD pipelines.
• Deploy ML models in production environments.
• Use distributed training frameworks to help scale projects.
• Develop and manage Terraform libraries for infrastructure.
• Oversee security monitoring and infrastructure maintenance.
• Collaborate with diverse, globally-distributed teams.

Essential Skills

Experience:

• 3+ years of hands-on experience in MLOps.
• Master's or Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

Technical Skills:

• Proven ability to design and implement cloud solutions, including building MLOps pipelines across the entire ML project lifecycle (data management, experimentation, model training, deployment, and monitoring) on AWS in production.
• Experience with one or more MLOps frameworks (e.g., Kubeflow, MLFlow, SageMaker, DataRobot, Airflow, Dagster).
• Fluency in Python and a solid understanding of Linux.
• Familiarity with machine learning frameworks like sci-kit-learn, Keras, PyTorch, TensorFlow, etc.
• Knowledge of DevOps principles, CI/CD pipeline design, data security, and cloud platform architecture.
• Strong understanding of fundamental computer science concepts, including common data structures and algorithms.
• Experience using AWS CloudFormation or Terraform for AWS service configuration.

Additional Skills:

• Ability to monitor MLOps tools and approaches and actively identify new solutions to domain-specific challenges.
• Familiarity with tools used by data scientists and experience in software development and test automation.

Soft Skills:

• Excellent English language skills.
• Ability to collaborate effectively within diverse, globally distributed teams.

Time Zone: Preference for candidates in GMT+0 to GMT+4.