- Design, develop, and deploy end-to-end ML models using modern ML frameworks.

- Build scalable ML pipelines including data preprocessing, feature engineering, model training, evaluation, and monitoring.

- Lead experimentation for new ML approaches and drive improvements in model accuracy, latency, and reliability.

- Work closely with Data, Product, and Engineering teams to translate business problems into ML solutions.

- Develop high-quality, production-ready Python code, ensuring robustness, performance, and maintainability.

- Implement automated model retraining, A/B testing, and continuous model optimisation.

- Mentor junior ML engineers and contribute to ML best practices, architecture, and code reviews.

- Work on large-scale datasets to derive insights, build features, and improve ML performance.

- Own the full ML lifecycle from research to deployment in production environments.

Requirements :

- 7+ years of experience in Machine Learning, Data Science, or Applied ML Engineering.

- Strong expertise in ML model development: classification, regression, anomaly detection, deep learning, and NLP (any subset is fine).

- Proven experience building ML systems that are running live in production.

- Excellent coding skills in Python and knowledge of production ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM).

- Experience with distributed systems, large-scale data processing, or feature store solutions.

- Hands-on experience with MLOps tools (Airflow, MLflow, Kubernetes, Docker, CI/CD).

- Strong understanding of data structures, algorithms, and software engineering principles.

- Ability to design scalable architecture and optimise ML models for performance.

- Experience working with cloud platforms (AWS/GCP/Azure).

- Solid analytical thinking, problem-solving skills, and attention to detail.