AI Solutions Engineer

1 week ago

Greenville, South Carolina, United States Vertisystem Full time

Qualifications:

Delivers machine learning production solutions to facilitate the deployment and management of ML Models within a cloud infrastructure. Engages with product stakeholders, data analysts, business strategists, and data architects to design, construct, and enhance platforms and services in an AI-driven cloud ecosystem. Capable of articulating business challenges and formulating solutions in real-time. Analyzes datasets to define problems and assess their implications. Possesses a solid understanding of the data science problem life cycle and has hands-on experience with real-world machine learning applications. Interprets machine learning metrics effectively. Collaborates with business users to assess their challenges and datasets for viable machine learning opportunities.

Requirements:

  • In-depth comprehension of business challenges and the ability to translate them into machine learning solutions.
  • Experience in deploying machine learning models into operational environments.
  • Research-oriented background.
  • Focus primarily on machine learning and business-related initiatives.
  • Experience in the manufacturing sector is a notable advantage.
  • Ability to collaborate with business teams and integrate machine learning solutions into ongoing projects.
  • Expertise in Generative AI, including use case development, enhancement of existing applications, and creation of new templates.
  • Proficiency in Python and machine learning methodologies is crucial.
  • Experience in Natural Language Processing (NLP) is beneficial.
  • Willingness to work two days a week.

Key Responsibilities:

  • Identify business challenges and devise machine learning solutions.
  • Deploy and oversee models in a production setting.
  • Collaborate with business teams to ensure seamless integration of machine learning solutions.
  • Concentrate on generative AI applications and the refinement of existing systems.

A day in the life includes:

  • Working with extensive datasets and preparing data for analysis.
  • Evaluating the effectiveness and reliability of data sources.
  • Engaging with business users to gain deeper insights into data sources, asking pertinent questions to understand data utility.
  • Assessing the outcomes of machine learning algorithms to determine their interpretability.
  • Evaluating the feasibility of use cases based on business challenges and available data.
  • Monitoring and analyzing model performance.
  • Conducting prompt engineering tasks tailored to specific use cases.

Education:

  • Bachelor's degree in computer science, mathematics/statistics, or a related discipline.
  • Advanced degree (Master's or PhD) in computer science, mathematics/statistics, or a related field is preferred.

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