Mandatory Skills
The Team:
Works with stakeholders across all pillars of the Global Financial Markets (GFM) Department to create proprietary algorithms and data science solutions for trading, marketing, customer personalization and process optimization.
We are seeking an MLOps Engineer who thrives in dynamic environments, possesses a strong foundation in engineering and systems design with a keen interest in building robust ML systems and pipelines in production.
Responsibilities:
- 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.
Required Experience:
- 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.
