Vice President, AI / Machine Learning Software Engineer at BNY Mellon

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 and champions of inclusion, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #Life AtBNY 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 Engineering team. This role is located in Pune.

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

• Drive hands-on quality engineering for AI/ML model and AI based application validation. Build and maintain automation assets and enhance test frameworks that improve reliability, speed, and confidence in releases across pods. Partner closely with engineering and data science to embed measurable quality gates into CI/CD.

• Architect and optimize end-to-end CI/CD pipelines for AI/ML model deployment, embedding advanced automation, policy-based gates, and environment parity checks.

• Lead design and implementation of reusable automation solutions (test libraries, templates, pipeline steps, containerized runners) supporting multiple pods and diverse AI use cases.

• Drive rigorous model evaluations for upgrades and new AI solutions, including shadow testing, performance and robustness stress tests.

• Analyze quality metrics and pipeline telemetry to identify systemic issues; recommend and implement strategic improvements (flaky test reduction, deterministic builds, artifact traceability).

• Expand automation coverage continuously (functional, non-functional, data quality, governance checks) and close gap areas via prioritized backlogs.

• Establish observability for quality (dashboards, alerts); codify automation code rollback strategies and fail-safe mechanisms.

• Partner with platform engineering, security, data governance, and model risk stakeholders to align quality gates with standards and risk controls. Share best practices of AI assisted quality management.

• 80% and above should be done using AI tools - windsurf, Eliza and Digital Quality Engineer.

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

Required Qualifications:

• Bachelor’s/Master’s in CS/Engineering or equivalent experience.

• 8–12 years in quality engineering with deep expertise in CI/CD for data/ML systems and distributed services.

• Proven track record in building scalable automation frameworks and pipelines in regulated or mission-critical environments.

Core Skills & Tools:

• JAVA Selenium, Python, PyTest, GitHub/GitLab CI, CI/CD pipeline; SQL; API testing tools (Postman/REST assured); performance testing (Jmeter or equivalent - preferred, observability (Prometheus/Grafana), JIRA, AI assisted tools

• Github copilot, Windsurf, Codex etc.

• Success Metrics:

• Pipeline reliability: reduced failure rates and faster, consistent build-to-deploy times.

• Quality gate efficacy: improved prevention of substandard model releases; measurable reduction in post-deploy incidents.

• Reuse and standardization: adoption of shared automation assets across pods; reduction in duplicated effort.

• Data-driven improvements: demonstrable quality uplift guided by telemetry and KPIs (defect trend, test flakiness, mean time to restore).