Overview

This role is for one of the Weekday's clients

Responsibilities

• Define and execute the organization's long-term AI and Machine Learning strategy aligned with business objectives.
• Lead the design, development, deployment, and optimization of AI-powered products and intelligent systems.
• Build, mentor, and scale high-performing teams of AI engineers, Machine Learning engineers, Data Scientists, and research professionals.
• Drive innovation by identifying emerging AI technologies and evaluating their business applications.
• Oversee the complete AI lifecycle, including data collection, feature engineering, model development, deployment, monitoring, and continuous improvement.
• Collaborate with product, engineering, and business teams to integrate AI capabilities into customer-facing and internal platforms.
• Establish best practices for model governance, experimentation, data quality, and responsible AI development.
• Ensure AI systems are scalable, secure, reliable, and optimized for production environments.
• Communicate AI strategies, technical decisions, and project outcomes effectively to executive leadership and cross-functional stakeholders.
• Foster a culture of continuous learning, experimentation, and innovation across the AI organization.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related technical field. A PhD is a plus.
• 712 years of professional experience in Artificial Intelligence, Machine Learning, and Data Science, with demonstrated leadership experience.
• Proven track record of delivering AI-driven products or platforms at scale.
• Strong experience managing technical teams and leading cross-functional initiatives from concept to production.
• Excellent communication, stakeholder management, and strategic planning skills.
• Artificial Intelligence
• Machine Learning
• Data Science
• Predictive Modeling
• Statistical Analysis
• Model Development and Evaluation
• Python and AI development frameworks
• Data Processing and Feature Engineering
• MLOps concepts and model deployment
• Cloud-based AI platforms
• Team Leadership and Technical Mentoring
• AI Strategy and Product Innovation
• Large Language Models (LLMs)
• Machine Learning and Deep Learning
• Generative AI applications
• Natural Language Processing (NLP)
• Computer Vision
• Reinforcement Learning
• Transformer architectures
• Retrieval-Augmented Generation (RAG)
• AI Agents and Autonomous Systems
• Distributed Model Training
• GPU Computing and Model Optimization