Head of Machine Learning

Role & responsibilities

• Define and lead the organization's Machine Learning and AI strategy in alignment with business objectives.
• Build, manage, and mentor high-performing Machine Learning, Data Science, and AI engineering teams.
• Lead the development and deployment of production-grade ML models and AI solutions across business use cases.
• Identify opportunities to apply machine learning, Generative AI, NLP, computer vision, and predictive analytics to drive measurable business impact.
• Establish ML architecture, development standards, model lifecycle practices, and MLOps frameworks.
• Oversee the complete ML lifecycle from problem definition, experimentation, and model development to deployment, monitoring, and continuous improvement.
• Drive adoption of LLMs, RAG, fine-tuning, AI agents, and other emerging AI technologies where relevant.
• Partner with Product, Engineering, Data, Analytics, and Business teams to translate business problems into scalable AI solutions.
• Ensure ML systems meet requirements for accuracy, scalability, reliability, security, explainability, and responsible AI.
• Define and track ML/AI performance metrics and demonstrate measurable business outcomes.
• Evaluate new ML frameworks, platforms, models, and cloud technologies to maintain a competitive technology advantage.
• Establish practices for model governance, monitoring, experimentation, versioning, reproducibility, and responsible AI.
• Manage technical roadmaps, budgets, resources, priorities, and delivery timelines.
• Communicate AI strategy, technical direction, risks, and business impact to senior leadership and executive stakeholders.

Preferred candidate profile

• 6 to 11 years of experience in Machine Learning, Data Science, AI Engineering, or a related field, with demonstrated technical and/or people leadership experience.
• Proven track record of delivering machine learning and AI solutions into production at scale.
• Strong hands-on expertise in Python and ML frameworks such as PyTorch, TensorFlow, or equivalent.
• Strong knowledge of supervised and unsupervised learning, deep learning, statistical modelling, experimentation, and model evaluation.
• Experience with Generative AI, LLMs, RAG, prompt engineering, fine-tuning, AI agents, NLP, or computer vision is highly preferred.
• Strong understanding of MLOps, model deployment, monitoring, CI/CD, model versioning, and ML lifecycle management.
• Experience with cloud platforms such as AWS, Azure, or GCP and technologies such as Docker and Kubernetes.
• Strong understanding of ML system design, scalable model serving, data pipelines, feature engineering, and model performance optimization.
• Experience leading, mentoring, and hiring high-performing ML/AI teams.
• Strong business acumen with the ability to translate AI opportunities into measurable commercial or operational outcomes.
• Excellent communication, stakeholder management, strategic thinking, and executive presentation skills.
• Ability to balance research/innovation with practical, production-focused AI delivery.
• Bachelor's degree in Computer Science, AI/ML, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Master's degree or PhD in AI/ML, Computer Science, Data Science, Statistics, or a related field is preferred, particularly for research-intensive roles.