Role Purpose
The incumbent shall be responsible for leading AI/ML operational and platform
engineering functions across the organization. The focus of this role is to provide and
ensure technical excellence in the AI/ML technology landscape and drive the adoption
of AI solutions across business units.
Requirements
- 10 years experience in IT, software engineering, or data science related positions
- 5 years of direct experience with AI/ML technologies, platforms, and solutions in
production environments.
- 3 years in technical leadership, architecture, or senior engineering role.
- Experience in Designing and implementing large-scale AI/ML solutions and
systems.
- Experience working with cloud platforms and understanding of cloud-native
architectures.
- Experience with DevOps, CI/CD pipelines, and containerized deployments
Duties and Responsibilities
KEY ACCOUNTABILITIES/KRAs/KPIs
- Deep dive technical investigation, analysis and troubleshooting across AI/ML
technology stacks, frameworks, and infrastructure using appropriate diagnostic
and monitoring tools.
- Provide technical leadership and mentoring to AI Engineering, Data Engineering,
and Platform teams, fostering a culture of technical excellence and continuous
improvement.
- Drive the enablement of AI/ML platforms, tooling, and best practices across the
organization.
- Provide architecture guidance and technical oversight for AI/ML solution design,
implementation, and optimization.
- Lead the evaluation, selection, and integration of AI/ML tools, frameworks, and
cloud services (e.g., Azure AI, AWS SageMaker, Google Vertex AI).
- Establish and maintain monitoring, logging, and observability standards for AI/ML
systems and models.
- Investigate opportunities for optimization of AI/ML technology stacks, including
model performance tuning and infrastructure efficiency.
- Work with solution architects and business stakeholders to translate business
requirements into technical AI/ML solutions.
- Provide support and enablement for containerized and cloud-native environments,
specifically Kubernetes and serverless platforms.
- Ensure compliance, security, and governance best practices are implemented
across all AI/ML solutions.
- Stay current with emerging AI/ML technologies, frameworks, and industry best
practices.
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