Machine Learning Engineer

• We are looking for a Senior Machine Learning Engineer (MLE) to join our Risk Data Science team. You will play a key role in designing, building, deploying, and scaling ML models that drive credit risk, fraud prevention, behavioral scoring, and other risk-related decision systems across our business. • You will work closely with data scientists, risk analysts, and engineering teams to transform research prototypes into high-performance, production-grade solutions that operate at scale in real-time decisioning environments. • Model Deployment & Scaling • Productionise risk and fraud models developed by the DS team using robust, efficient, and maintainable architectures • Design low-latency, high-availability APIs and pipelines for real-time model inference. • Implement batch scoring systems for periodic risk assessments.= • MLOps & Infrastructure • Build and maintain CI/CD pipelines for model deployment and monitoring. • Set up automated feature engineering pipelines, leveraging feature stores. • Ensure model governance: reproducibility, versioning, auditability, and compliance with regulatory requirements. • Model Monitoring & Maintenance • Implement real-time and batch monitoring for data drift, concept drift, and model performance. • Build automated retraining workflows and model rollback mechanisms. • Collaboration with Risk DS • Work closely with risk data scientists to translate experimental code (Python, notebooks) into production-grade services. • Advise DS on efficient model architectures for operational environments. • Optimize feature computation for speed and scalability. • System Design & Integration • Integrate models with credit underwriting, fraud detection, collections, and merchant risk systems. • Collaborate with backend engineering to align on API contracts and system interfaces. • Your Expertise • 6+ years of experience as an MLE, ML Engineer, Mlops Developer. • Strong Python skills (including Pandas, NumPy, scikit-learn, PySpark, FastAPI/Flask). • Proficiency in distributed computing frameworks (Spark, Ray) and workflow orchestration tools (Airflow, Prefect). • Experience with MLOps tools (MLflow, SageMaker, Vertex AI, or similar). • Strong understanding of model deployment in cloud environments (AWS/GCP/Azure). • Solid knowledge of microservice architecture, containerization (Docker), and orchestration (Kubernetes). • Proven track record of deploying and maintaining ML models in production at scale. • Experience in building and integrating with real-time streaming systems (Kafka, Kinesis, Pub/Sub). • All qualified individuals are encouraged to apply.