Python and AI programming expert (SME)

Role: IT Python and AI programming expert
Location: Washington DC, Eagan MN, St Louis MO, Wilkes-Barre PA, San Mateo CA, Raleigh or Morrisville NC, or Remote

Duration: 12 months
Client: IPS/USPS

Note: candidates meet the residency requirements (Last 5 years in the US with no more than 6 months out of the country).

List of tasks to be performed:

• 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.

• 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.

• 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.