ml engineer for production AI

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• Design, develop, and productionize machine learning models across training, validation, deployment, monitoring, and retraining
• Lead AI use cases, including client lifetime value, churn prediction, and fraud or abuse detection
• Build and establish robust MLOps practices, including deployment pipelines, CI/CD, environment promotion, and model lifecycle management
• Implement model monitoring frameworks covering performance, data drift, data quality, and business impact
• Establish retraining and escalation strategies for production models
• Implement model explainability and transparency using SHAP, feature attribution, and other appropriate interpretability techniques
• Define and enforce best practices for model governance, documentation, versioning, and auditability
• Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility, and scalable data foundations
• Partner with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions
• Drive continuous improvement through monitoring insights, feedback loops, and model retraining
• Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery

• 5–8+ Years of production ML engineering experience
• Strong Python and software engineering fundamentals
• Strong ML fundamentals, including evaluation, feature engineering, leakage, drift, and stability
• Hands-on experience with Spark / PySpark
• Experience with MLflow or equivalent
• Experience building CI/CD pipelines, preferably with GitHub Actions
• Strong SQL and experience with large-scale datasets
• Proven delivery of production AI/ML solutions with measurable business impact

• Flight ticket and two weeks of accommodation
• Employee Training & Development programme
• Stebby Sports Compensation or Confido Health Compensation
• Multiple team and group events
• Birthday and loyalty benefits