Machine Learning Engineer
Core
Design, build, test, deploy, and maintain machine learning models and AI-driven solutions, translating business problems into practical ML/AI applications.
Role type
Machine Learning Engineer
Builds
Production ML models, APIs, and services for business applications
Domain
Banking, fintech, risk, fraud, payments, customer analytics
Deliverable
production ML models
Required skills
Python, SQL, machine learning algorithms, statistical modelling, feature engineering, model evaluation, MLOps practices, cloud platforms
Preferred skills
Scala, R, Java, C++, MLflow, Docker, Kubernetes, Airflow, Databricks, SageMaker, Azure ML, Vertex AI, generative AI, RAG, vector databases, LangChain, model explainability
Technologies
Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Pandas, NumPy, Spark, PySpark, Git, REST APIs, AWS, Azure, GCP
Responsibilities
Develop ML pipelines for training, testing, deployment, monitoring, and retraining; Build APIs or services to expose ML models; Perform feature engineering, data preparation, experimentation, and model evaluation; Support MLOps practices including model versioning, monitoring, CI/CD, and automation; Monitor model performance, data quality, model drift, and production behaviour; Document model logic, technical designs, deployment processes, and support procedures
Seniority
Mid-level (3+ years experience)
