The Role
We are seeking a highly skilled and experienced Hands-On Technical Lead to join our AI Center. The ideal candidate is a Data Scientist with a proven track record of deploying real-world AI applications at scale in large organizations. This role requires an individual who is deeply passionate about the operationalization of AI models within both data and technical ecosystems and has strong leadership skills to oversee and mentor a team of ML engineers. The primary focus will be on setting the framework to implement AI and support a path to production, working collaboratively with other GM teams to transition their ideas from PoCs to real-world applications.
What You’ll Do
- Framework Development: Develop and set up frameworks and methodologies that enable GM teams to follow best practices in AI model deployment and operations.
- Collaborative Implementation: Work closely with Data Scientists across GM to help them transition their ideas from PoCs to production-ready AI solutions.
- Lead the Deployment of AI Solutions: Oversee the end-to-end deployment of AI, custom AI, and Generative AI models, ensuring seamless integration and flawless operation within our data and technical ecosystems.
- Team Leadership: Lead, mentor, and manage a team of ML engineers, fostering a collaborative and innovative environment. Provide guidance and support to team members to ensure project success and professional growth.
- Azure and Databricks Expertise: Utilize Azure for storage and Databricks for compute, ensuring efficient and scalable data processing and model training.
- ML Operations with ML Flow: Implement and manage ML Flow as the core of our ML operations, maintaining high standards for model tracking, reproducibility, and deployment.
- Code Quality and Compliance: Establish CI/CD pipelines with GitHub Enterprise, ensuring rigorous code quality, and registering ethics and bias artifacts to comply with GM's AI policy.
- Hands-On Coding: Write high-quality code, contributing directly to the development and deployment of AI models using Python.
- Code Scanning and Quality Assurance: Utilize code scanning tools like SonarQube to ensure code quality and adherence to best practices.
- End-to-End Timing Charts: Create and manage end-to-end timing charts to monitor and optimize the performance of AI models.
- Solution Design: Design solutions with an emphasis on overall maintainability and scalability.
- AI Thought Leadership: Stay conversant with current AI topics, trends, and advancements, bringing innovative ideas to the AI Center.
- Cross-Functional Collaboration: Work closely with data engineers, software developers, and business stakeholders to translate business needs into technical solutions.
Your Skills & Abilities (Required Qualifications)
- Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
- Experience: Minimum of 5 years of experience in data science, with a strong emphasis on deploying AI applications at scale in large organizations.
- Technical Skills:
- Proficiency in programming languages such as Python.
- Extensive experience with Azure and Databricks.
- Deep understanding of ML Flow for managing ML operations.
- Strong knowledge of CI/CD practices and tools, particularly GitHub Enterprise.
- Experience with code scanning tools like SonarQube.
- Ability to create and manage end-to-end timing charts.
- Operational Excellence: Demonstrated obsession with the operationalization of AI models, ensuring flawless execution and integration.
- Ethics and Compliance: Experience in registering and managing code quality, ethics, and bias artifacts.
- Leadership Skills: Proven experience in leading and mentoring a team of engineers, with strong interpersonal and communication skills.
- Soft Skills: Excellent problem-solving skills, effective communication abilities, and a collaborative mindset.
