Responsibilities include deploying and monitoring machine learning models in production (on-premises and in cloud environments); developing and optimizing data processing and feature engineering pipelines; building APIs and microservices to support machine learning models; implementing model performance monitoring and drift detection; writing clean, efficient, and well-documented code; conducting code reviews and mentoring junior engineers; and performing data analysis to generate insights for business decisions.

Qualifications include a Bachelor's degree in computer science, software engineering, data science, or a related field; a minimum of 2 years of experience in software engineering or data science; proficiency in Python, Java, or C++; knowledge of Pandas, NumPy, and Scikit-learn; understanding of ML concepts, algorithms, model evaluation metrics, and feature engineering; experience with SQL, database management, and Linux; proficiency with Git and version control; familiarity with cloud platforms (Azure, AWS, Google Cloud), big data technologies; familiarity with Docker, Kubernetes, Apache Kafka, ML Ops tools (MLflow), and CI/CD concepts.

Benefits include medical, dental and vision insurance; company-paid life insurance; 401(k) with company match; paid

Responsibilities include deploying and monitoring machine learning models in production (on-premises and in cloud environments); developing and optimizing data processing and feature engineering pipelines; building APIs and microservices to support machine learning models; implementing model performance monitoring and drift detection; writing clean, efficient, and well-documented code; conducting code reviews and mentoring junior engineers; and performing data analysis to generate insights for business decisions.

Qualifications include a Bachelor's degree in computer science, software engineering, data science, or a related field; a minimum of 2 years of experience in software engineering or data science; proficiency in Python, Java, or C++; knowledge of Pandas, NumPy, and Scikit-learn; understanding of ML concepts, algorithms, model evaluation metrics, and feature engineering; experience with SQL, database management, and Linux; proficiency with Git and version control; familiarity with cloud platforms (Azure, AWS, Google Cloud), big data technologies; familiarity with Docker, Kubernetes, Apache Kafka, ML Ops tools (MLflow), and CI/CD concepts.

Benefits include medical, dental and vision insurance; company-paid life insurance; 401(k) with company match; paid