AI programming expert - Databricks

Hi,

Hope you are doing well

Role:  AI programming expert - Databricks
Location: Remote
Duration: 12 months

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

Chosen resources 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.

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.