Associate Director

Key Responsibilities

• Lead the design, development, and deployment of Generative AI models (e.g., language models, Advanced RAG) for diverse business applications.
• Manage, mentor, and inspire a team of data scientists and machine learning engineers, fostering a culture of collaboration, continuous learning, and excellence.
• Collaborate with cross-functional teams—including product, engineering, and business stakeholders—to define project objectives, requirements, and deliverables.
• Oversee the full project lifecycle from data collection, model development, validation, deployment, to ongoing monitoring and improvement.
• Stay abreast of the latest advancements in Generative AI and machine learning, evaluating and implementing new technologies as appropriate.
• Ensure best practices in coding, model evaluation, reproducibility, and documentation within the team.
• Present findings, project progress, and business impact to both technical and non-technical audiences.

Required Qualifications

• Master’s or PhD in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 12+ years of Industry experience with 5+ years of experience in data science or machine learning, with at least 2 years leading teams or managing projects.
• Demonstrated expertise in Generative AI technologies (e.g., GANs, VAEs, Transformer-based models, LLMs).
• Proficiency in Python and relevant ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
• Strong understanding of end-to-end ML pipelines including data preprocessing, cloud based (AWS, Azure, GCP) model development, deployment.
• Experience deploying AI solutions in production environments (cloud or on-premises).
• Excellent communication, leadership, and stakeholder management skills.

Qualifications

• Experience with MLOps tools and practices for scalable model deployment and monitoring.
• Track record of publishing work in top AI conferences or journals.
• Experience working with large-scale datasets and high-performance computing environments.
• Knowledge of data privacy, security, and ethical considerations in AI deployment.