- Design AI/ML pipelines and engineering infrastructure to support internal and external machine learning systems at scale.
- Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. (inc. data drift, model retraining, setting up rollbacks)
- Deploy AI/ML models, optimizing algorithm and system efficiency through GPU distributed computing and concurrent programming.
- Create reusable libraries/ microservices, deploy machine learning ecosystems, and integrate subsystems as necessary.
- Stay updated with the latest trends and technologies in AI/ML, GenAI, MLOps and LLMOps to drive innovation and system enhancements.
- Experience building scalable machine learning system architectures (microservice, distributed, etc.) and big-data pipelines in production.
- Knowledge in the operationalization of Data Science projects (MLOps) using at least one mainstream framework or platform.
- Deep understanding and application of various machine learning and evaluation concepts, their mathematical underpinnings, and trade-offs.
- Exceptional programming skills in Python (Or demonstrable ability to pick up new languages) and SQL variants, with knowledge of design patterns, code optimization, and object-oriented design.
- Familiarity with software development best practices and tools such as Agile methodologies, Jira, Jenkins, and Git.
Good to have:
- Familiar with Linux OS, Openshift, Kubernetes for container-based deployment.
- Demonstrable expertise in econometrics, statistical modelling, time-series analysis, causal inference, and their applications to pricing and marketing domains.
- Hands-on experience in designing and executing digital experimentation and hypothesis testing, including A/B testing, bandit-based experiments, and multivariate analysis.
- Experience with language models, RAG concepts, opensource generative AI (GenAI) frameworks and prompt engineering principles.
- Prior experience / strong knowledge and understanding of the financial markets.
