Data Platform (AI/ML) Automation Senior Technical Lead (SVP) - Hybrid

We are seeking a highly motivated and experienced Data Platform (AI/ML) Automation Senior Technical Lead (Enterprise Business Architecture & Transformation Group Manager – C14) to lead the technical direction and implementation of AI/ML automation initiatives within our data platform, focusing on creating scalable, efficient, and production-ready machine learning systems. This role combines deep technical expertise with team leadership to drive innovation in ML automation and orchestration. 

Requires a comprehensive understanding of multiple areas within a function and how they interact to drive the design, development and implementation of our platform’s AI/ML automation capabilities. You will lead a team of regulatory and risk reporting subject matter experts and engineers in building robust, scalable and automated solutions.   

Applies in-depth understanding of the business impact of technical contributions.  Excellent commercial awareness is a necessity.  Generally accountable for delivery of a full range of services to one or more businesses/ geographic regions.  Excellent communication skills required to negotiate internally, often at a senior level.  Some external communication may be necessary.  Accountable for the end results of an area.  Exercises control over resources, policy formulation and planning.  Primarily affects a sub-function.  Involved in short- to medium-term planning of actions and resources for own area.   

Responsibilities:

• Leadership & Mentorship: Lead and mentor a team of data engineers, fostering a culture of collaboration, innovation, and continuous improvement. Provide technical guidance, conduct code reviews, and support their professional development. 

• Strategy & Architecture: Define and champion the technical vision and strategy for AI/ML automation within the data platform. Design and implement scalable and robust architectures for automated data pipelines, model training, and deployment. Contributes to the global implementation of common data and data standards, common processes and integrated technology platforms 

• Development & Implementation: Build and optimize data pipelines to support AI/ML initiatives. Collaborate with engineering teams to develop end-to-end solutions that integrate data ingestion, processing, and model deployment. Utilize best practices for CI/CD, DevOps, and MLOps, ensuring seamless integration with existing data platform infrastructure 

• Technology Evaluation & Selection: Evaluate and select appropriate technologies and tools for AI/ML automation, considering factors such as scalability, performance, cost, and maintainability.  

• Performance Optimization: Identify and address performance bottlenecks in data pipelines and model training processes. Implement optimizations to improve efficiency and scalability. 

• Monitoring & Maintenance: Develop and implement monitoring and alerting systems to ensure the reliability and performance of automated data pipelines and AI/ML models. Troubleshoot and resolve issues as they arise. 

• Collaboration & Communication: Collaborate effectively with cross-functional teams, including data scientists, product managers, and business stakeholders, to understand requirements and communicate technical concepts clearly. Mentor team members and provide technical leadership in ML engineering and automation practices 

• Documentation & Best Practices: Develop and maintain comprehensive documentation for AI/ML automation processes and regulatory compliance. Contribute to the development of internal knowledge sharing resources. 

• Innovation & Research: Explore and experiment with new AI/ML techniques and technologies to drive innovation and improve our data platform capabilities. Establish technical standards and best practices for ML automation, ensuring reliability, scalability, and maintainability.

Qualifications:

• 10+ years of experience in Finance projects, with a strong focus on strategic architecture and AI/ML solutions deployment in the finance domain.

• Must have experience in applications development, focusing on end-to-end solutions which are fully aligned to business capabilities.

• Must have experience in leading AI/ML Finance projects, particularly in developing and implementing anomaly detection capabilities for Finance both structured/unstructured use cases. Prefer hands on experience with daily regulatory based financial reporting anomaly detection and remediation with unsupervised modeling techniques.

• Strong leadership and team management skills, with the ability to mentor and gu