Senior ML Engineer

• Own and deliver solutions within the scope of the role, from requirements and technical/design decisions through implementation and continuous improvement.
  
• Work closely with engineering, product, data, design, and business stakeholders to translate requirements into practical, scalable solutions.
  
• Apply strong engineering and/or domain expertise to build reliable, maintainable, and production-ready capabilities.
  
• Contribute to architecture, standards, documentation, quality, and technical decision-making appropriate to the role.
  
• Identify performance, scalability, data quality, usability, reliability, or operational risks and address them proactively.
  
• Collaborate across teams to ensure solutions integrate effectively with existing systems and platform components.
  

Requirements

• Experience: 5+ years of relevant professional experience.
  
• Strong hands-on experience with: Applied machine learning, tabular predictive modelling, feature engineering, gradient boosting, model evaluation and calibration, Python, Spark, MLOps.
  
• Applied machine learning with models running in production, not research or proof of concept.
  
• Deep hands on with tabular predictive modelling on customer data.
  
• Has built churn or propensity models in telco, banking, or retail.
  
• Training, deployment, and retraining pipelines in a self managed environment.
  
• MLOps practice: model registry, versioning, retraining, monitoring, and drift detection.
  
• Comfortable working inside a data platform rather than a notebook.
  

• Telco or Banking is a must
  

• Uplift or causal modelling for incremental targeting.
  
• Feature store design.
  
• Working with commercial stakeholders on what a prediction is used for.
  

Originally posted on Himalayas