AI programming expert - Databricks

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).

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.

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.

Data science tasks

• 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.