Director, AI/ML Technology and Architecture

.AI/ML Architecture & Strategy 

• Define and evolve the long-term AI/ML architecture roadmap, ensuring scalability, maintainability, and integration with existing systems. 

• Conduct thorough technology evaluations and vendor assessments to identify and select the most suitable AI/ML tools, platforms, and technologies. 

• Develop and implement best practices, guidelines, and standards for AI/ML development and deployment across the organization. 

• Collaborate with business stakeholders to understand their needs and translate them into clear and actionable AI/ML strategies. 

• Drive innovation by exploring and evaluating emerging AI/ML technologies and their potential applications within the media and entertainment domain. 

2. AI/ML Platform & Infrastructure 

• Design, build, and maintain a robust and scalable AI/ML platform that supports the entire lifecycle of AI/ML model development, from data ingestion and feature engineering to model training, deployment, and monitoring. 

• Ensure the security, reliability, and performance of the AI/ML infrastructure, including data privacy, compliance, and disaster recovery. 

• Optimize resource utilization and cost-effectiveness of the AI/ML platform through efficient resource allocation and infrastructure management. 

• Collaborate with IT and cloud operations teams to ensure seamless integration and smooth operation of the AI/ML platform. 

• Monitor and analyze platform performance, identify bottlenecks, and implement necessary improvements to enhance efficiency and scalability. 

3. AI/ML Model Development & Deployment 

• Oversee the development and deployment of high-quality and impactful AI/ML models across various domains, such as content recommendation, personalization, content creation, and fraud detection. 

• Ensure the quality, accuracy, and fairness of AI/ML models through rigorous testing, validation, and monitoring. 

• Develop and implement MLOps best practices, including CI/CD pipelines, automated testing, and model monitoring, to streamline the model development and deployment process. 

• Collaborate with data scientists and engineers to ensure efficient and effective model training, deployment, and maintenance. 

• Drive the adoption of AI/ML models across the organization through clear communication, training, and support. 

4. Team Leadership & Management 

• Lead and mentor a high-performing team of AI/ML engineers and architects. 

• Foster a collaborative and innovative team culture that encourages experimentation, learning, and growth. 

• Recruit, hire, and onboard top talent in the field of AI/ML. 

• Provide guidance and support to team members on technical challenges and career development. 

• Track team performance, identify areas for improvement, and implement necessary changes to enhance team effectiveness. 

5. Governance and Compliance 

• Ensure all AI/ML initiatives adhere to regulatory requirements, ethical guidelines, and company policies. 

• Define processes for monitoring, auditing, and improving AI models in production. 

• Advocate for responsible AI practices within the organization.