At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption. We’re a leading RegTech firm working with large international financial institutions such as Standard Chartered Bank and HSBC. Join us and help make the world a safer place!
As a Python Developer, you will create and manage applications that bolster our AML solutions and refine machine learning models to improve product functionality. You will work closely with data scientists to implement these models in production environments and design systems for data integration and quality assurance. Furthermore, you will handle the entire machine learning pipeline, from data preprocessing to model deployment, ensuring that applications are scalable and efficient.
Responsibilities
Develop and maintain Python-based applications and tools that support our AML solutions
Implement, optimize, and deploy machine learning models, including large language models, to enhance our product capabilities
Collaborate with data scientists and cross-functional teams to integrate machine learning 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 customers and internal stakeholders to drive product development
Analyze and process large-scale structured and unstructured data sets to support machine learning initiatives
Ensure the robustness, scalability, and efficiency of the developed applications and models
Provide technical support and troubleshoot issues related to machine learning models and Python applications
Follow best practices in coding, MLOps, and DataOps
Assist other teams in delivering proof-of-concept projects
Requirements
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
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