6+ years of industry experience, including software development in Machine Learning, Deep Learning, ML infrastructure, and working with large data sets and systems;
Proficiency in cloud AWS computing platforms, focusing on deploying and managing machine learning and deep learning models;
Experience working with AWS cloud computing services, leveraging their capabilities for efficient machine learning deployments;
Proficiency in Python, SQL programming languages;
Proficiency in Restful API development, specifically with frameworks like FastAPI;
Familiarity with containerization technologies, including Docker, Kubernetes, or AWS ECS, to facilitate easy deployment and management of ML applications;
Experience with data Engineering and data Pipelines is a must as technology such as ( Airflow, MWAA, Mage, Ray, etc.);
Ability to navigate and discuss various options for CI/CD, experimentation, and model training on AWS cloud infrastructure;
Sound understanding of end-to-end deployed machine learning services and pipelines, including testing, monitoring, and addressing challenges like model drift and coverage;
Familiarity with version control systems like Git to effectively manage code repositories and collaborate with team members;
Ability to implement Terraform or Cloudformation pipelines and prior experience working with infrastructure-as-code tools;
Experience in scaling and deploying recommendation systems, ensuring their efficient operation and performance;
These qualifications will position you to effectively contribute to our team, designing, deploying, and scaling machine learning solutions using cloud platforms, automation, and ML/Ops best practices;
Excellent oral and written English communication skills.

RESPONSIBILITIES

Collaborate with data scientists to understand the use case and design a deployment system that best suits their machine learning models;
Working closely with the platform and Data Ops teams to establish continuous integration and continuous deployment (CI/CD) processes for the data science and machine learning team;
Consider cost optimization and implementing automation techniques to minimize expenses where applicable;
Provide training and mentorship to data scientists on deployment best practices and model training techniques;
Develop and maintain automated ML pipelines for efficient model training, testing, and deployment;
Collaborate with software engineers and DevOps teams to ensure seamless integration of ML models into production systems;
Simplifying feature engineering processes, ensuring data consistency during model training, and serving through integrated feature stores;
Scale and deploy infrastructure to support both batch and real-time inference scenarios;
Identify, document, automate, and engineer processes for repeated tasks;
You will participate in the engineering Scrum ceremonies and take the initiative to develop ML/Ops product architecture.