Senior 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.

Your Responsibilities
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.