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Serving as the technical authority on AI model design, architecture, and integration within the product ecosystem.
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Leading the evaluation, selection, and implementation of AI/ML frameworks, tools, and best practices, ensuring scalability, robustness, and maintainability.
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Participating in hands-on coding, solution prototyping, and code reviews to maintain high quality standards and guide the team through complex technical challenges.
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Overseeing and refining the AI development lifecycle, including model training, validation, deployment, and ongoing improvement.
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Leading a team of AI engineers, providing mentorship, regular feedback, and career development support.
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Defining clear performance expectations, conduct performance reviews, and identify growth opportunities for team members.
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Fostering a positive, inclusive, and high-performance team culture that encourages innovation, continuous learning, and collaboration
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Collaborating with recruitment and HR to identify, attract, and retain top AI engineering talent, ensuring the team’s ongoing growth and success
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Leading a team of AI engineers, providing mentorship, regular feedback, and career development support
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Defining clear performance expectations, conduct performance reviews, and identify growth opportunities for team members
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Fostering a positive, inclusive, and high-performance team culture that encourages innovation, continuous learning, and collaboration
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Collaborating with recruitment and HR to identify, attract, and retain top AI engineering talent, ensuring the team’s ongoing growth and success
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Working closely with the Director of AI Development, Product Managers, Designers, Data Scientists, and other Engineering leaders to translate business requirements into technical roadmaps and actionable engineering plans
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Ensuring seamless integration of AI capabilities into existing and future products, partnering with platform, infrastructure, and DevOps teams to optimize deployment and operations
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Communicating technical topics effectively to non-technical stakeholders, making recommendations and reporting progress, risks, and opportunities
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Implementing and continuously improving the development processes, standards, and tools that drive efficiency, reliability, and scalability.
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Ensuring adherence to best practices in model governance, performance monitoring, and compliance with relevant data privacy and security regulations
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Monitoring engineering metrics and KPIs, leveraging data-driven insights to improve development velocity, code quality, and team efficiency