Machine Learning Lead

Role & responsibilities

• Lead the design, development, training, and deployment of machine learning models to solve complex business problems.
• Define machine learning strategies, technical roadmaps, and best practices aligned with organizational objectives.
• Manage and mentor a team of machine learning engineers and data scientists, fostering technical excellence and innovation.
• Oversee the end-to-end ML lifecycle, including data preparation, feature engineering, model selection, experimentation, evaluation, deployment, and monitoring.
• Develop and optimize predictive models, classification algorithms, recommendation systems, and forecasting solutions.
• Drive the adoption of deep learning, natural language processing (NLP), computer vision, and generative AI techniques where appropriate.
• Establish MLOps practices to automate model training, testing, deployment, versioning, and monitoring.
• Collaborate with data engineering, software engineering, product, and business teams to translate business requirements into scalable ML solutions.
• Monitor model performance, accuracy, data drift, bias, and reliability, implementing improvements as required.
• Optimize model performance, inference latency, scalability, and computational costs in production environments.
• Evaluate emerging ML frameworks, algorithms, tools, and technologies to improve model effectiveness and development efficiency.
• Ensure adherence to data privacy, security, responsible AI, and model governance standards.
• Communicate technical findings, model outcomes, and business impact to senior management and cross-functional stakeholders.
• Manage project timelines, technical deliverables, resource allocation, and quality standards across machine learning initiatives.

Preferred candidate profile

• Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, or a related technical discipline.
• 5 to 10 years of experience in machine learning, data science, or AI engineering, with demonstrated technical leadership and production deployment experience.
• Strong proficiency in Python and SQL, with hands-on experience using PyTorch, TensorFlow, and Scikit-learn.
• Solid understanding of statistical modeling, probability, linear algebra, optimization, and machine learning algorithms.
• Proven experience in feature engineering, model training, hyperparameter tuning, evaluation, and performance optimization.
• Experience with deep learning, NLP, computer vision, recommendation systems, or generative AI, depending on business requirements.
• Hands-on experience deploying and maintaining production-grade machine learning models and scalable ML pipelines.
• Strong knowledge of MLOps tools and practices, including MLflow, Kubeflow, model versioning, automated testing, CI/CD, and model monitoring.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform, along with Docker and Kubernetes.
• Experience with data processing frameworks, distributed computing, APIs, and production software engineering practices.
• Demonstrated ability to mentor engineers, conduct code and model reviews, establish technical standards, and lead complex ML projects.
• Strong analytical thinking, problem-solving, communication, stakeholder management, and decision-making skills.
• Ability to translate business objectives into measurable machine learning solutions and communicate technical concepts to non-technical stakeholders.
• Relevant AI/ML, cloud, or MLOps certifications are advantageous; a postgraduate degree is preferred but not mandatory.