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.
1. Model Development & Innovation
- 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.
2. ML Infrastructure & Pipelines
- 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.
3. Production Deployment & Monitoring
- 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.
4. Cross-Functional Collaboration
- 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.
Requirements
Technical Expertise
- 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.
Production Experience
- Proven track record of deploying and maintaining ML models in production environments.
- Familiarity with cloud platforms (AWS/GCP/Azure) and scalable data architectures.
Corporate Acumen
- 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.
Benefits