Machine Learning Engineer

The Expertise And Skills You Bring

• Demonstrated hands on experience in the design and architecture of large-scale systems.
• Proven experience in developing ML infrastructure in the Cloud (AWS preferred) for DataOps, MLOps and DevOps including creation of automated CI/CD pipelines for ML models and deploying ML solutions to production.
• Solid experience with building data pipelines in getting the data required to build, train and evaluate ML models, using tools like Apache Spark, Snowflake or other distributed data processing frameworks and CI/CD tools (e.g., Jenkins or equivalent), version control (GitHub), Artifactory.
• Proven experience in Object Oriented Programming, functional scripting languages like JavaScript (NodeJs), Unix scripting or related programming languages and exposure to some of Python’s ML ecosystem (numpy, pandas, scikit-learn, sklearn, tensorflow, pytorch).
• Solid knowledge of Cloud computing and infrastructure as code (IaS), hands-on experience with developing, deploying and managing applications in the Cloud.
• You have working knowledge with Cloud Technologies such as Docker, Kubernetes, AWS CLI, AWS CloudFormation, and AWS services including EC2, EMR, EKS, S3 and Big Data ML toolkits, such as SparkML and Amazon SageMaker.
• Knowledge of applied data science methods, feature engineering and machine learning algorithms, and usage of word embeddings in Natural Language Processing.
• Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.).