ML Engineer

As a Machine Learning Architect, your primary responsibilities will include: - Understanding the current state architecture, including pain points - Creating and documenting future state architectural options to address specific issues or initiatives using Machine Learning - Innovating and scaling architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization - Developing CI/CD & ML pipelines to achieve end-to-end ML model development lifecycle - Providing recommendations around security, cost, performance, reliability, and operational efficiency and implementing them - Offering thought leadership on the use of industry standard tools and models by leveraging experience and current industry trends - Collaborating with stakeholders to establish and implement strategic initiatives - Making recommendations, assessing proposals for optimization, and identifying operational issues to resolve problems Qualifications required for this role: - 3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development - Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others - Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment - Strong programming skillset with high proficiency in Python, R, etc. - Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc. - Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop - Knowledge of pure and applied math, ML and DL frameworks, and ML techniques such as random forest and neural networks - Ability to collaborate with Data scientist, Data Engineers, Leaders, and other IT teams - Ability to work with multiple projects and work streams simultaneously and deliver results based upon project deadlines - Willingness to flex daily work schedule to allow for time-zone differences for global team communications - Strong interpersonal and communication skills As a Machine Learning Architect, your primary responsibilities will include: - Understanding the current state architecture, including pain points - Creating and documenting future state architectural options to address specific issues or initiatives using Machine Learning - Innovating and scaling architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization - Developing CI/CD & ML pipelines to achieve end-to-end ML model development lifecycle - Providing recommendations around security, cost, performance, reliability, and operational efficiency and implementing them - Offering thought leadership on the use of industry standard tools and models by leveraging experience and current industry trends - Collaborating with stakeholders to establish and implement strategic initiatives - Making recommendations, assessing proposals for optimization, and identifying operational issues to resolve problems Qualifications required for this role: - 3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development - Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others - Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment - Strong programming skillset with high proficiency in Python, R, etc. - Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc. - Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop - Knowledge of pure and applied math, ML and DL frameworks, and ML techniques such as random forest and neural networks - Ability to collaborate with Data scientist, Data Engineers, Leaders, and other IT teams - Ability to work with multiple projects and work streams simultaneously and deliver results based upon project deadlines - Willingness to flex daily work schedule to allow for time-zone differences for global team communications - Strong interpersonal and communication skills