Machine Learning Engineers (no c2c)

Job Description: Theoris Services is looking to hire two Machine Learning Engineers to help stand up the ML Environment at our client. This is a fully remote role for people who are able to support Eastern Time Zone core hours.

Job Responsibilities:

• Contribute to the design and development of cutting-edge machine learning infrastructure and platform to train and serve models at scale, enabling data scientists to utilize the latest techniques in their models.
• Implement machine learning pipeline and workflows.
• Deploy, monitor, and maintain machine learning models in production systems.
• Build continuous integration and delivery pipelines for machine learning applications.
• Communicate progress updates and problem-solving activities with a team of data scientists, data engineers, and stakeholders.
• Scale the organization's ability to reuse models, features, and code in machine systems for enterprise use.
• Develop and implement scalable and efficient modeling algorithms that can work with large-scale data in production systems.
• Develop predictive models on large-scale datasets to address various business problems using advanced statistical modeling, machine learning, and analytics techniques.
• Ensure compliance with all processes, procedures, and standards applicable to the position, including but not limited to System Organization Controls (SOC 1), NERC Critical Infrastructure Protection (CIP), National Institute of Standards and Technology (NIST) risk management framework, change management practices, and regulatory standards in the energy industry.

Requirements:

• Statistical programming expertise.
• Proficiency in R and Python.
• Experience with predictive modeling, data cleaning, data visualization, segmentation modeling, natural language processing, graph database knowledge, API usage, database querying, software development, and applied mathematical modeling.
• Proficiency with at least 5-7 of the following technical capabilities: Advanced Excel, Tableau, Power BI, PowerApps, Power Automate, Apache Spark, Databricks, Azure Data Factory, Azure Machine Learning Studio, SQL, Docker and/or Kubernetes.