Overview
Skills
Job Details
Job Description:
- 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, TensorFlow, or sciket-learn.
Duties and Responsibilities
- 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, Google Cloud Platform), 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 ecosystems.
API development and integration.
Querying and managing data in both SQL and NoSQL databases.
Works on high-visibility, or mission critical aspects of a given program, and performs all functional duties independently.
Oversees the efforts of direct reporting resources and/or be responsible for the efforts of all staff assigned to a specific job.
Tasks might include (neither exhaustive nor restrictive):
Data science tasks such as data acquisition, data cleaning, and feature extraction.
Develop and demonstrate proof-of-concepts (PoC); independently or in a team.
Create technical diagrams and documentation to show PoC implementations and potential production implementation.
Researching and presenting to teammates on the latest tools/packages/capabilities being developed.
Make recommendations on relevant tools/packages to use for production environments.
Work with relevant governance committees to document and obtain approval for exploratory data science efforts.
Consulting with members of architecture teams to identify potential automation solutions which may include AI/ML.
Collaborating with cross functional teams on holistic AI/ML solutions.
Education:
A minimum of thirteen (13) to twenty (20) years relevant experience.
o A degree from an accredited College/University in the applicable field of services is preferred. four additional years of relevant experience in lieu of a college degree is required. If the individual's degree is not in the applicable field then four additional years of related experience is required.