AI Director - NLP / Computer Vision

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 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.g., 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 Qualifications :
• Masters 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.

(ref : hirist.tech)