Vice President, Ai

5 days ago

Pune, Maharashtra, India CyOpsPath Full time

Job description & requirements

Vice President, AI / Machine Learning Software Engineer

At BNY,
our culture allows us to run our company better and enables employees' growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world's investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.

Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary.

We're seeking a future team member for the role of Vice President, AI / Machine Learning Software Engineer to join our team in Fund & Investor Solution Platform team. This role is located in PUNE

In this role, you'll make an impact in the following ways:

Lead the strategic direction and execution of AI/ML initiatives across the organization.

Drive innovation in machine learning models, deep learning architectures, and AI-powered solutions.

Collaborate with cross-functional teams to identify opportunities for AI/ML integration in business processes.

Oversee the development, deployment, and scaling of ML models in production environments.

Mentor and grow a high-performing team of data scientists, ML engineers, and researchers.

Ensure ethical AI practices and compliance with data privacy regulations.

To be successful in this role, we're seeking the following:

Master's or PhD in Computer Science, Mathematics, Statistics, or related field.

12+ Years of experience required relevant to Job Profile.

Strong foundation in mathematics, including linear algebra, probability, statistics, and optimization.

Proven analytical thinking and problem-solving capabilities.

Extensive hands-on experience with Python and PyTorch for building and training ML/DL models.

Deep understanding of supervised, unsupervised, and reinforcement learning techniques.

Experience with model evaluation, tuning, and deployment in cloud environments (AWS, Azure, GCP).

Familiarity with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, CI/CD pipelines).

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