Lead - ML Engineer

About the Role:

You will be working on ensuring scaling of ML products and credit risk policy (incl. models) within pre-approved/ pre-qualified campaigns and live digital journeys. It will entail ensuring APIs operate smoothly at scale, identify ways to ensure simplified and low risk customer journey. We are looking for someone with entrepreneurial mindset, cloud-based tools/ packages for automation and encryption and APIs.

About the team:

You will be part of ML Engineering Team within Digital Banking Unit of the bank. You will work in a fast-paced environment where new ideas are encouraged. This will involve liaising with teams within Digital and those across the bank e.g. data engineers, data scientists, product managers and senior business and credit risk stakeholders.

Education, Work Experience, Key Skill Set Requirements:

Education:

Masters or Undergraduate in Computer Science/Electrical/Civil/Mechanical.

Experience:

68 years of overall industry experience, with at least 34 years in Machine Learning (ML)/MLOps, or Business Rule Engine (BRE) implementation.

Technical Expertise:

• ML/MLOps System Design: Proven experience in end-to-end ML/MLOps system architecture including model/BRE implementation (batch/API/cloud), debugging, and production maintenance.
• Programming & Data Tools: Proficient in Python, PySpark, NumPy, Pandas, SQL, and Linux. Good to have Koalas, Dask.
• Cloud & Big Data: Hands-on experience with Azure cloud services including Azure Databricks, Spark, Data Warehousing, and pipeline orchestration.
• CI/CD & DevOps: Skilled in Azure DevOps for CI/CD pipeline setup and management, including automated model deployment.
• API Development: Experience with FastAPI/Flask for building scalable ML models APIs.

Domain Knowledge:

• Exposure to credit risk modeling and financial services is a strong plus.

Soft Skills:

• Excellent communication and stakeholder management.
• Strong project management and cross-functional collaboration abilities.