AI Manager

Key Responsibilities
• Strategic Leadership: Define the roadmap, scope, and objectives for enterprise AI and machine learning initiatives.
• Cross-Functional Collaboration: Serve as the primary bridge between technical teams (data science, MLOps, software engineering) and non-technical business stakeholders.
• Team Management: Hire, mentor, and manage a team of AI/ML specialists, providing coaching, performance management, and career development.
• Model Lifecycle & Operations: Oversee the operational lifecycle of AI models from deployment to monitoring, data drift tracking, and retirement.
• Governance & Compliance: Coordinate risk assessments, model validation, bias testing, and data privacy compliance.
• Performance Metrics: Track operational metrics including latency, model availability, accuracy, and business ROI.
• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Statistics, or a related technical field.
• Technical Literacy: Deep understanding of AI/ML architectures, machine learning lifecycles (MLOps), and modern frameworks (e.g., PyTorch, TensorFlow, LangChain, or cloud AI services on AWS, Google Cloud Platform, or Azure).
• Project Management: Proven track record of delivering complex technical projects on time and within budget using Agile or traditional project management methodologies.
• Communication Skills: Exceptional ability to explain complex algorithmic concepts clearly to executive leadership, product teams, and clients.
• Experience: 5+ years of experience in data science, machine learning, or AI engineering, with at least 2+ years in a leadership or management capacity. Microsoft Open AI experience.