Machine Learning Engineer

Our client is seeking a Machine Learning Engineer with expertise in Databricks, Python, AWS, MLOps, GenAI, Recommedations, NLP. Must be able to work independently, without much supervision.

Responsibilities:

• In new product design roles: develops and programs integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments.
• Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms.
• In product/systems improvement projects: uses machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy.
• In both theoretical development environments and specific product design, implementation and improvement environments, uses current programming language and technologies to translate algorithms and technical specifications into code.
• Completes programming and implements efficiencies, performs testing and debugging.
• Completes documentation and procedures for installation and maintenance.
• Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations.
• Adapts machine learning to areas such as virtual reality, augmented reality, artificial intelligence, robotics and other products that allow users to have an interactive experience.
• Can work with large scale computing frameworks, data analysis systems and modeling environments.

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Typically requires a Bachelors Degree and minimum of 5 years directly relevant work experience Note: One of the following alternatives may be accepted: PhD or Law + 3 yrs; Masters + 4 yrs; Associates degree + 6 yrs; High School + 7 yrs.

The target hiring compensation range for this role is $57.60 to $64 an hour. Compensation is based on several factors including, but not limited to education, relevant work experience, relevant certifications, and location.