
Associate / Senior Associate , Forensic, Artificial Intelligence, Risk Consulting (MJ004522)
- Hong Kong
- Permanent
- Full-time
- Deploying machine learning models into production environments using tools like Docker, Kubernetes, and cloud services (AWS, Azure, GCP)
- Using cloud services (AWS, Azure, GCP) for deploying and managing machine learning workflows
- Git for version control and collaboration
- Tools such as MLflow, Kubeflow, or similar for managing the end-to-end machine learning lifecycle.
- RESTful APIs for the integration of machine learning models into product environments
- Large Language Models (LLM)/GenAI technology like OpenAI API, ChatGPT, GPT-4, Bard, Synthesia, Langchain, HuggingFace Transformers, PyTorch
- Monitoring tools to track model performance, detect data drift, and trigger model retraining if necessary (e.g., Prometheus, Grafana).
- Understand KPMG's values
- Produce impeccable work products
- Manage global clients across different sectors
- Harness cutting edge technology in your day-to-day work
- Understand financial crime red flags and behaviours
- Apply critical thinking and initiative to problem solving
- To be the best version of yourself
- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or related field.
- Exposure in machine learning or data science, with a proven track record of managing end-to-end model lifecycle.
- Prior experience with cloud platforms and containerization technologies.
- Recent graduates or candidates with less than 2 years of experience will be considered if they have project experience or strong references.
- Proven expertise in Python and machine learning libraries (TensorFlow, PyTorch, Scikit-learn).
- Solid understanding of data structures, algorithms, and software engineering principles.
- Experience with MLOps tools and practices, including CI/CD, model versioning, and monitoring.
- Strong analytical and problem-solving abilities.
- Excellent communication skills, capable of explaining complex technical details to non-technical stakeholders.
- Proficiency in SQL, and experience with big data technologies (e.g., Hadoop, Spark).
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