JOB  DETAILS

Key Responsibilities:

• Lead GenAI and Agentic system design and development: Prototype, design, develop, deploy and design monitoring for innovative GenAI and Agentic solutions that reimagine the customer service experience.
• Develop and implement Machine Learning Models: Prototype, design, develop, deploy and design monitoring for a variety of machine learning models.
• Expand Data/AI Science Usage: Identify potential AI opportunities across COO for future focus.
• Drive Continuous Improvement and Business Outcomes: work closely with operations teams to understand their experience, pioneering adoption and application of transformative AI and Gen AI capabilities and paving the path for how the bank will operate to towards the vision of re-imagined banking and customer service.

We’re interested in hearing from people with the following skills:

Technical Leadership and Coordination:

• Ability to coordinate effectively across the COO Data Science community to unify efforts and drive collaborative outcomes.
• Demonstrated capability in driving outcomes that are reusable and can be leveraged across the group.
• Intellectual curiosity and ability and willingness to learn, experiment and lead new to the world concepts

Technical Skills and Tools:

• Generative AI: Experience designing and deploying various aspects of Generative AI, including prompt engineering, Retrieval-Augmented Generation (RAG), guardrail design, orchestration design, skills, evals frameworks, and tools such as LangChain and LangGraph.
• Machine Learning: Expertise in the design and construction of machine learning models, including natural language processing, unsupervised clustering and classification algorithms including feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment of models into production environments.
• Cutting-Edge AI: Work on projects leveraging traditional AI, Generative AI, and agentic AI to pave the path for how banking and broader industry functions in the future.
• Version Control and CI/CD: Experience with version control and CI/CD pipelines such as GitHub.
• Tooling: Hands-on experience with data science tools such as Spark, Python, R, TensorFlow, PyTorch, and SQL.
• Data Engineering: Knowledge of ETL processes and data pipeline construction, ensuring efficient data flow and integration.
• Deployment: Familiar with Docker for containerization and deployment of applications.
• Cloud Architecture: Grounded understanding in AWS or Azure Cloud architecture and integration, demonstrating experience in deploying and managing cloud-based solutions.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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