ML Engineer

Company Overview

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Job Summary

We are seeking an ML Engineer with strong expertise in deploying, monitoring, and managing machine learning pipelines on Snowflake. The ideal candidate will act as a bridge between the Data Engineering team and the ML team, ensuring seamless integration of ML models into production workflows.

Responsibilities

• Deploy, monitor, and optimize ML models on Snowflake.
• Design and maintain end-to-end ML pipelines including training, validation, deployment, and monitoring.
• Use MLflow for experiment tracking, model registry, and performance monitoring.
• Collaborate with Data Engineers on data ingestion, transformations, and model-ready datasets.
• Implement MLOps best practices such as CI/CD for ML, model versioning, and reproducibility.
• Support performance tuning of ML models and pipelines.
• Provide governance and documentation for ML workflows.

Qualifications

• Technical Skills:

  • ML Engineering: Proficient in ML pipeline design, model deployment, monitoring, and retraining.
  • Snowflake: Strong knowledge of the Snowflake platform, Snowpark, and SQL.
  • MLflow: Hands-on experience with experiment tracking, model registry, and monitoring.
  • Programming: Proficient in Python, including libraries such as pandas, numpy, scikit-learn, and PySpark.
  • DevOps/MLOps: Skilled in CI/CD pipelines, version control (GitHub), containerization (Docker), and orchestration (Airflow/Databricks workflows).

• Educational Qualifications:

  • Bachelor's or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.

Preferred Skills

• Experience working in a collaborative team environment.
• Strong analytical and problem-solving abilities.
• Knowledge of additional programming languages such as R or Scala is a plus.
• Understanding of data governance frameworks.

Experience

• 5–8 years of relevant experience in ML engineering, particularly in deploying and managing machine learning workflows.

Environment

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Salary

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Growth Opportunities

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Benefits

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