September 17
• Develop and maintain Python-based applications and tools that support AML solutions • Implement, optimize, and deploy machine learning models to enhance product capabilities • Collaborate with data scientists and cross-functional teams to integrate models into production systems • Design and implement data integration, validation, and quality check frameworks • Work on the entire machine learning pipeline, from data preprocessing to model deployment and monitoring • Create quick prototypes and present them to stakeholders to drive product development • Analyze and process large-scale structured and unstructured datasets to support machine learning initiatives • Ensure the robustness, scalability, and efficiency of applications and models • Provide technical support and troubleshoot issues related to machine learning models and applications • Follow best practices in coding, MLOps, and DataOps • Assist other teams in delivering proof-of-concept projects
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field • Strong proficiency in Python programming and related libraries (e.g., Pandas, NumPy, scikit-learn) • Experience in implementing and deploying machine learning models, including large language models (e.g., GPT, BERT) • Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers • Experience with data preprocessing, feature engineering, and model evaluation techniques • Proficiency in working with large-scale datasets and SQL • Knowledge of MLOps practices and tools for continuous integration and deployment of machine learning models • Experience in designing and implementing data integration and validation frameworks • Strong problem-solving skills and attention to detail • Excellent communication skills to collaborate with cross-functional teams and present technical concepts to non-technical stakeholders • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies
• 100% remote work forever • Paid Development Days • In-house training • Team building events • Weekly "Tea Time" to catch up
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