ML Platform Engineer-(Pyhton) – Ironscales

About the job
IRONSCALES Fights Phishing. IRONSCALES is a self-learning, AI-driven email security solution that continuously detects and remediates advanced threats for global organizations of any size. Our solution is fast to deploy, easy to operate, and provides unparalleled protection against email threats.


Who We Are: We are IRONSCALES. We care about people. We care about cybersecurity. We care about our customers and partners. Our team acts with intentionality and our actions are always in the best interest of our teams, our customers, and our company. Our culture is focused on innovation, continuous improvement, and the drive to push boundaries and take everything to the next level. We are a rapidly growing team and welcome all who love a fast-paced, rewarding challenge to join our team today!


Who You Are: You are driven, self-motivated, and have a strong desire to achieve the company’s growth goals. You are resourceful and accountable, hungry to chase down answers and solve challenges. You are respectful and a good communicator. You are detail oriented and thrive in a fast-paced environment. You love meeting new people and are motivated by developing others.


What You Will Do: As a ML Platform engineer you will work closely on a cross functional team with ML engineers and Data engineers to deploy real time models at scale and build systems and tooling to support advanced uses cases and increase development velocity. You will build the next generation ml platform developing systems for ml development lifecycle, continuous training, real time feature stores and more.


Core Responsibilities Include:

Design the data pipelines and engineering infrastructure to support machine learning systems at scale
Develop and deploy scalable tools and services to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems
Support model development, with an emphasis on reproducibility, versioning, and data quality
Communicate with stakeholders to build requirements and track progress


What We Need from You:

Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
Strong software engineering skills in complex real time systems
Fluency in Python
Experience working with AWS cloud technologies such Sagemaker or equivalent
Familiarity with orchestration tools such as airflow and mlflow
Exposure to machine learning methodology and best practices

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