Senior Machine Learning Engineer

Job Purpose

Key Accountabilities

• Architect ML systems / end-to-end pipelines / scalable and production-ready solutions
• Lead model development/pricing and ranking algorithms / improved conversion and revenue yield
• Design forecasting solutions/demand and supply models / optimized inventory and occupancy planning
• Enhance risk models/fraud detection systems / reduce cancellations and policy violations
• Establish MLOps standards/deployment and monitoring frameworks / reliable and maintainable model operations
• Drive experimentation / A/B testing and validation frameworks / data-driven product improvements
• Collaborate cross-functionally / product, engineering, and data teams / aligned metrics and business impact
• Define data strategy/feature engineering and data sourcing / improved model performance and coverage
• Ensure model governance/data quality and integrity / trusted and compliant ML systems
• Mentor team members / technical guidance and best practices / elevated team capability and output quality
• Optimize model performance/tuning and retraining strategies / sustained accuracy and efficiency
• Evaluate new approaches/algorithms and technologies / continuous innovation and competitive advantage
• Communicate insights/model performance and impact / informed stakeholder decision-making.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field
• 5–8+ years of experience in applied machine learning, data science, or ML engineering roles
• Proven experience designing, deploying, and scaling ML systems in production environments
• Strong experience with large-scale data processing and distributed systems (e.g., Spark, Beam)
• Advanced proficiency in Python and ML frameworks such as TensorFlow or PyTorch
• Deep understanding of MLOps practices (CI/CD, model lifecycle management, feature stores, monitoring)
• Strong expertise in SQL and data engineering tools (Airflow, Kafka, DBT, etc.)
• Experience with experimentation frameworks and causal inference methods is a plus
• Strong system design and architecture skills for ML-driven products
• Excellent problem-solving skills with a strong analytical and business-oriented mindset
• Ability to lead initiatives, influence stakeholders, and mentor junior team members