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

Senior AI Engineer

CIO Architecture, Strategy & Innovation (CASI) assists the Vice President of Information Technology (IT) in designing, developing, maintaining, supporting, and translating corporate business vision and strategy into objectives for building, enhancing, or replacing business and IT systems capabilities, along with implementing a comprehensive IT roadmap while maintaining resilience to change as a key architectural and strategy objective for the enterprise.

To support the Customer mission, projects, and system requirements, the CASI team demonstrates leadership by evaluating and driving implementation of innovative solutions for technology infrastructure and application architecture.

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.

• Chosen resource must demonstrate these capabilities through actual work experience not merely training:
  • 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.
  • Typically performs all functional duties independently.
• Chosen resource should exhibit through actual work experience not merely training:
  • Desirable: hands on experience building MCP servers and integration with Agentic AI workflows.
  • Communicating complex technical concepts to both technical and executive stakeholders.
  • Proficiency creating technical diagrams with products like Microsoft Visio or Draw.io.
  • Proficiency creating technical design and architecture documents in Microsoft Word.
  • Proficiency creating business and technical presentations in Microsoft PowerPoint.
  • Proficiency creating data representations, charts and reports in tools such as Microsoft's Excel worksheets and Power BI.
  • Ability to communicate, orally and in writing, sufficient to develop and present management briefings; provide written and/or verbal guidance on technical issues; and prepare/present recommendations and reports.
  • Using design patterns for building scalable and maintainable applications/solutions.
  • Clearly document code, models, and technical solutions.
  • Proficiency in Generative AI and prompt engineering.
  • Continuous learning and adaptability in a very large IT organization.
  • Troubleshooting software and technical implementations in large-scale enterprise…