Machine Learning Engineer

We are seeking a highly skilled and motivated Machine Learning Engineer to join our growing ML/AI practice.

This role focuses on enhancing machine learning systems for recommendation and personalization applications.

The ideal candidate will have hands-on experience in model development, performance evaluation, and production deployment, and will collaborate with cross-functional teams to deliver impactful AI solutions.

Key Responsibilities

• Iterate on existing ML models to improve performance, efficiency, and feature integration.
• Benchmark models and enhance blending strategies for multiple prediction sources.
• Evaluate model performance using offline metrics and online experiment data.
• Conduct subgroup analysis to identify areas for KPI improvement.
• Enhance model explainability to support product and business decision-making.
• Collaborate with data scientists, backend engineers, and other stakeholders.
• Contribute to AI model development and deployment using tools like Databricks, MLflow, and Seldon.
• Participate in code reviews and ensure adherence to coding standards.
• Drive innovation by proposing new ideas and improvements to data infrastructure and processes.

• Minimum 3 years of experience in machine learning or data science teams (excluding research/R&D roles).
• Minimum 5 years of experience programming in Python or Scala.
• Minimum 5 years of experience with ML libraries and frameworks (e.g., Tensor Flow, PySpark).
• At least 1 year of experience with Databricks, MLflow, or Seldon.
• Strong understanding of recommendation algorithms and machine learning principles.
• Experience with cloud services (e.g., AWS) and software engineering best practices.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.

• Proven track record of delivering large-scale commercial ML products.
• Experience with Snowflake, Kubeflow, Tecton, Jenkins.
• Background in building customer-facing ML applications, especially recommendation systems.
• Familiarity with custom ML platforms, feature stores, and model monitoring.
• Knowledge of A/B testing, causal inference, and ML best practices.
• Advanced degree (Master’s or Ph.D.) in Computer Science, Statistics, Data Science, or a related field.

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