Engineer, Senior AI; IT Python and AI programming; Expert III

Senior AI Engineer

Architecture, Strategy & Innovation (CASI) is responsible to assist Vice President of Information Technology (IT) design, develop, maintain, support, and translate corporate business vision and strategy along with stakeholder input into objectives for building, enhancing, or replacing business and IT systems capabilities, together with an implementation of a comprehensive IT roadmap, all-the-while maintaining resilience to change as a key architectural and strategy objective for the enterprise.

To support the mission, projects, and system requirements, the client requires sophisticated information technology systems support services by experienced professionals.

Designs and develops scalable solutions using AI tools and machine-learning models. Performs research and testing to develop machine learning algorithms and predictive models. Utilizes big data computation and storage tools to create prototypes and datasets. Conducts model training and evaluation. Integrates, tests, tunes, and monitors solutions. Proficient with multiple AI tools such as Python, Java, or R and machine learning frameworks like Spark, Tensor Flow, or sciket-learn.

Duties and Responsibilities:

• Practical Application of Core Python Concepts:
  Not just knowing Python syntax but demonstrating a track record of building and deploying Python applications or scripts that address IT operational needs, automate processes, or handle data management.
• Data Engineering and Analysis
  
  Skills:
  
  Demonstrable experience with data acquisition, cleaning, preprocessing, and transformation using Python tools and techniques for building robust analysis on large scale data sets.
• Implementing and Deploying Cloud Applications:
  Experience deploying python applications in cloud service production environments (e.g., AWS, Azure, GCP), potentially leveraging containerization tools (e.g., Docker, Kubernetes).
• Understanding of Software Engineering Best Practices:
  Experience in applying principles like version control (Git), writing clear and testable code, participating in code reviews, and using continuous integration/continuous deployment (CI/CD) pipelines.
• Knowledge of Data Science Best Practices:
  Demonstrated understanding and implementation of data science solutions such as data pipelining, feature engineering, or creation of Machine Learning Models.
• Familiarity with Cloud-based Data Science Services:
  Proficiency using managed AI/ML services provided by cloud platforms to streamline development, deployment, and management of data science applications.
• Ethical Practices and Security Knowledge: A demonstrated awareness and application of ethical guidelines for data science solutioning, including addressing bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions.

Education:

A minimum of eight (8) to twelve (12) years' relevant experience. A degree from an accredited College/University in the applicable field of services is required. If the individual's degree is not in the applicable field then four (4) additional years of related experience is required.