Key Responsibilities

• Architect end-to-end ML systems including feature reuse and online scoring services
• Develop propensity, uplift, and engagement models for automated workflows
• Implement AI-assisted tools for automated code refactoring and experiment tracking
• Establish MLOps standards for model drift monitoring, canary rollouts, and fallback mechanisms
• Collaborate with product and analytics teams to align ML outcomes with business goals
• Mentor junior engineers and set engineering best practices across the department

Essential

• Minimum of 8 years of experience in machine learning engineering or data-driven platform development
• At least 3 years of experience in a technical leadership or senior-level engineering role
• Proven expertise in feature engineering and the development of complex data pipelines
• Advanced proficiency in model training, evaluation, and real-time inference systems
• Strong software engineering background in Python, Java, or equivalent languages
• Experience with MLOps practices including continuous training and deployment (CI/CD for ML)
• Background in deploying models within regulated or high-reliability environments

Preferred

• Experience with personalization, recommendation engines, or automated decisioning platforms
• Familiarity with distributed systems and event-driven architectural patterns
• Hands-on experience using AI-assisted development tools to optimize engineering workflows
• Knowledge of model explainability and interpretability frameworks

Key Skills