Director - Artificial Intelligence

Key Responsibilities:

- Strategic Leadership: Define and drive a long-term AI engineering strategy and roadmap aligned with business goals and innovation objectives.

- Team Management: Lead, mentor, and expand a high-performing AI engineering team, fostering a culture of excellence, collaboration, and continuous innovation.

- Cross-functional Collaboration: Work closely with product, data science, platform, infrastructure, and business teams to identify impactful AI use cases and ensure seamless integration of AI solutions.

- Solution Architecture & Development: Oversee the architecture, design, development, and deployment of scalable AI/ML systems, including deep learning and generative AI models.

- Lifecycle Ownership: Own the complete AI/ML lifecycle - from data acquisition and model development to validation, deployment, monitoring, and retraining.

- Innovation Focus: Drive the adoption of emerging technologies, such as Generative AI, to enhance product capabilities and user experience.

- Quality & Compliance: Ensure all AI systems meet performance, reliability, security, and compliance standards.

Required Skills & Qualifications:

- 15+ years of total experience with at least 5+ years in AI/ML engineering leadership roles.

- Strong hands-on understanding of machine learning, deep learning, NLP, computer vision, and Generative AI.

- Proven experience in designing and deploying AI systems at scale in production environments.

- Proficient in AI/ML tools and frameworks (e. , TensorFlow, PyTorch, Scikit-learn, Hugging Face).

- Experience with cloud platforms (AWS, Azure, or GCP) and ML Ops practices.

- Familiarity with end-to-end data pipelines, model monitoring, and performance optimization.

- Excellent leadership, stakeholder management, and communication skills.

Preferred Qualifications:

- Master's or PhD in Computer Science, Artificial Intelligence, or a related field.

- Experience in AI strategy or innovation roles within fast-paced product or platform organizations.

- Exposure to ethical AI, model fairness, and explainability frameworks