Senior Machine Learning Engineer (Lead Level)

We are looking for a seasoned Machine Learning Lead to spearhead the development, deployment, and optimisation of ML-driven systems—especially focused on personalisation and recommendations. You will work closely with product, engineering, and data teams to craft intelligent experiences that deeply impact user engagement and business outcomes.

• Lead the end-to-end development of ML models for personalisation, ranking, recommendations, and user intent understanding.
• Evaluate various modelling approaches (e.g., deep learning, embeddings, hybrid recommenders) and select the best fit based on business needs.
• Drive experimentation, A/B testing, and model improvements using data-driven insights.

• Architect and maintain scalable data pipelines for model training, retraining, and batch/real-time inference.
• Implement MLOps best practices including CI/CD for ML, feature stores, model registries, and automated monitoring.
• Ensure system reliability, low latency, and efficient resource utilisation.

• Own the deployment of ML models into production, ensuring high performance and stability.
• Build robust monitoring systems to detect drift, quality degradation, and performance bottlenecks.
• Continuously improve model accuracy, relevance, and business impact.

• Work closely with Product Managers to translate business goals into technical requirements.
• Partner with Engineering to integrate ML models seamlessly into user-facing features and backend services.
• Guide junior ML engineers and data scientists through mentorship and technical leadership.

• 5+ years of experience in Machine Learning, with strong hands-on work in recommendation systems and predictive modelling.
• Strong proficiency in Python, SQL, and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience working with large-scale datasets, embeddings, vector search, and distributed computing (Spark, Ray, etc.).
• Solid understanding of MLOps practices—Docker, Kubernetes, model registries, automated pipelines, observability tools.

• Proven track record of deploying and maintaining ML models in production environments.
• Familiarity with cloud platforms (AWS/GCP/Azure) and scalable data architectures.

• Strong problem-solving mindset with the ability to balance speed and quality.
• Excellent communication skills to work with cross-functional partners.
• Ability to lead, mentor, and guide team members.