Design and develop recommendation systems for personalized content and advertising.
Build, maintain, and optimize end-to-end machine learning pipelines.
Develop custom deep learning models from scratch.
Build data ingestion, validation, cleansing, and monitoring pipelines.
Train, validate, evaluate, and deploy machine learning models into production.
Monitor model performance using key performance indicators (KPIs) and continuously improve model accuracy.
Develop transformer-based and multi-armed bandit models.
Build proof-of-concept solutions for future machine learning initiatives.
Document technical designs, research findings, implementation details, and best practices.
Support testing, evaluation, and reporting of machine learning solutions.
Collaborate with cross-functional teams to deliver scalable, production-ready AI solutions. Required Qualifications
3-5 years of experience in Machine Learning Engineering, Data Science, or Data Engineering.
Strong hands-on programming experience with Python.
Recent hands-on experience with PySpark.
Experience with AWS cloud services.
Hands-on experience with Databricks.
Strong understanding of machine learning and deep learning fundamentals.
Experience building, training, evaluating, and deploying machine learning models into production.
Experience developing and supporting data engineering pipelines for machine learning applications.
Strong software engineering, analytical, and problem-solving skills.
Ability to develop scalable, production-ready machine learning solutions. Preferred Qualifications
Experience developing recommendation systems.
Experience with transformer-based models.
Experience with multi-armed bandit models.
Experience developing custom deep learning models.
Experience with data mining and statistical analysis.
Experience with Apache Kafka and Apache Spark.
Experience with Docker.
Experience with Java or Scala.
Experience building proof-of-concept machine learning solutions.
Experience supporting the complete machine learning lifecycle, including data engineering, model development, production deployment, monitoring, and optimization.

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